CN117975281B - Method for detecting damage source, electronic equipment and storage medium - Google Patents
Method for detecting damage source, electronic equipment and storage medium Download PDFInfo
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- 230000006378 damage Effects 0.000 title claims abstract description 222
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000001514 detection method Methods 0.000 claims abstract description 69
- 230000009528 severe injury Effects 0.000 claims abstract description 19
- 208000027418 Wounds and injury Diseases 0.000 claims description 29
- 208000014674 injury Diseases 0.000 claims description 29
- 230000003902 lesion Effects 0.000 claims description 13
- 238000012549 training Methods 0.000 claims description 10
- 208000037974 severe injury Diseases 0.000 claims description 7
- 238000002372 labelling Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 3
- 238000007796 conventional method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000006424 Flood reaction Methods 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- G06V20/10—Terrestrial scenes
- G06V20/176—Urban or other man-made structures
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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Abstract
The invention relates to a damage source detection method, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring remote sensing images before and after the damage of the detection area, and identifying house information in the remote sensing images of the detection area before the damage; converting the remote sensing image into a gray level image, and respectively extracting gray level coordinate sets of house areas in the gray level image before and after the damage; determining a pre-damage m-level degree score according to house information and a gray scale coordinate set of a house area in the pre-damage gray scale image; determining post-damage m-level degree scores according to house information and gray scale coordinate sets of house areas in the post-damage gray scale images; determining a damage degree score of each house according to the m-level degree score before damage and the m-level degree score after damage; determining a severe injury center coordinate, a distribution center coordinate and a damage center coordinate according to the damage degree score and house information in the remote sensing image of the detection area before damage; and determining the damage source coordinate according to the serious damage center coordinate, the distribution center coordinate and the damage center coordinate.
Description
Technical Field
The invention relates to the technical field of house damage source detection, in particular to a damage source detection method, electronic equipment and a storage medium.
Background
Due to natural disasters such as earthquakes, hurricanes, floods, and artifacts such as poor building quality, aging, etc., houses may suffer serious damage; conventional damage detection methods typically require manual field inspection, which is time consuming and labor intensive, and can be extremely dangerous to access to the damaged area in the event of an emergency after a disaster has occurred. Therefore, the rapid and accurate identification and assessment of the damage condition of the house is an important link in the disaster response and recovery process, and in view of the situation, the provision of the house damage source detection technology based on the remote sensing image for rapidly analyzing the remote sensing data, accurately identifying the damaged house and assessing the damage degree is particularly important for providing key information for emergency response and recovery work.
Disclosure of Invention
Based on the above problems, the present invention provides a method for detecting a source of damage, an electronic device, and a storage medium.
In a first aspect, an embodiment of the present invention provides a method for detecting a source of a lesion, including:
acquiring remote sensing images before and after the damage of the detection area, and identifying house information in the remote sensing images of the detection area before the damage by using a pre-trained house area detection model;
converting the remote sensing image into a gray level image, and respectively extracting gray level coordinate sets of house areas in the gray level image before and after the damage of any house;
Determining a pre-damage m-level degree score according to house information in the pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in the pre-damage gray scale image;
Determining a post-damage m-level degree score according to house information in the pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in the post-damage gray scale image;
Determining a damage degree score for each house according to the pre-damage m-level degree score and the post-damage m-level degree score;
Determining a severe injury center coordinate, a distribution center coordinate and a damage center coordinate according to the damage degree score of each house and the house information in the remote sensing image of the detection area before damage;
determining a damage source coordinate according to the severe damage center coordinate, the distribution center coordinate and the damage center coordinate;
Wherein the method comprises the steps of Is the gradation sequence number.
Further, in the above method for detecting a source of injury, the training step of the pre-trained house area detection model includes:
collecting remote sensing images of houses and labeling the houses in the images;
And training the marked house image by using a training model based on the YOLO to obtain a house area detection model.
Further, in the method for detecting a source of damage, the m-level degree score before damage is determined according to the house information in the remote sensing image of the detection area before damage and the gray scale coordinate set of the house area in the gray scale image before damage by the following formula:
wherein, house information in the remote sensing image of the detection area before damage is that ，For the serial number of the house in the remote sensing image,For the number of houses in the remote sensing image,The left vertex coordinates of the box are identified for house number i,The width of the box is identified for the ith house,A high of the block is identified for the ith house,For the pre-lesion m-level degree score,For the pre-lesion m-level degree score,To a gray scale coordinate set of a house area within a pre-lesion gray scale image,The pixel point coordinates of the house area in the gray level image,，。
Further, in the method for detecting a source of damage, the m-level degree score after damage is determined according to the house information in the remote sensing image of the detection area before damage and the gray scale coordinate set of the house area in the gray scale image after damage by the following formula:
wherein, house information in the remote sensing image of the detection area before damage is that ，For the serial number of the house in the remote sensing image,For the number of houses in the remote sensing image,The left vertex coordinates of the box are identified for house number i,The width of the box is identified for the ith house,A high of the block is identified for the ith house,For the m-level degree score after the damage,For the m-level degree score after the damage,In order to obtain the gray scale coordinate set of the house area in the damaged gray scale image,The pixel point coordinates of the house area in the gray level image,，。
Further, in the above method for detecting a source of damage, the damage degree score of each house is determined according to the m degree score before damage and the m degree score after damage by the following formula:
Wherein, For the m-level degree score after the damage,For the pre-lesion m-level degree score,For the score of the extent of the lesion,In order to classify the sub-score,Is the gradation sequence number.
Further, in the above method for detecting a source of damage, the coordinates of the center of severe damage, the coordinates of the center of distribution and the coordinates of the center of damage are determined according to the score of the degree of damage of each house and the information of the house in the remote sensing image of the detection area before damage by the following formula:
Wherein, The weight of the injury judging factor is that,The coordinates of the center of the injury are as follows,In order to obtain the distribution center coordinate as follows,In order to destroy the central coordinate of the device,A damage level score for each house i,To detect house information in the remote sensing image of the area before damage,For the serial number of the house in the remote sensing image,For the number of houses in the remote sensing image,The left vertex coordinates of the box are identified for house number i,The width of the box is identified for the ith house,A high of block is identified for house number i.
Further, in the above method for detecting a source of damage, the source of damage coordinate is determined according to the center coordinates of severe damage, the center coordinates of distribution and the center coordinates of damage by the following formula:
The coordinates of the center of the injury are as follows, In order to obtain the distribution center coordinate as follows,In order to destroy the central coordinate of the device,Is the source coordinate of the injury.
In a second aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory;
the processor is configured to execute a method for detecting a source of injury according to any one of the above by calling a program or instructions stored in the memory.
In a third aspect, embodiments of the present invention further provide a computer-readable storage medium storing a program or instructions that cause a computer to execute a damage source detection method according to any one of the above.
The embodiment of the invention has the advantages that: the method comprises the steps of acquiring remote sensing images before and after the damage of a detection area, and identifying house information in the remote sensing images of the detection area before the damage by using a pre-trained house area detection model; converting the remote sensing image into a gray level image, and respectively extracting gray level coordinate sets of house areas in the gray level image before and after the damage of any house; determining a pre-damage m-level degree score according to house information in a pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in a pre-damage gray scale image; determining m-level degree scores after damage according to house information in the remote sensing image of the detection area before damage and a gray scale coordinate set of a house area in the gray scale image after damage; determining a damage degree score of each house according to the m-level degree score before damage and the m-level degree score after damage; determining a severe injury center coordinate, a distribution center coordinate and a damage center coordinate according to the damage degree score of each house and the house information in the remote sensing image of the detection area before damage; and determining the damage source coordinate according to the serious damage center coordinate, the distribution center coordinate and the damage center coordinate. The method accurately determines the coordinates of the house damage source, thereby providing data assistance for staff to analyze the house damage source.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments or the conventional techniques of the present invention, the drawings required for the descriptions of the embodiments or the conventional techniques will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is a schematic diagram of a method for detecting a source of injury according to an embodiment of the present invention;
FIG. 2 is a schematic diagram II of a method for detecting a source of injury according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein without departing from the spirit or essential characteristics thereof and, therefore, the invention is not limited by the specific embodiments disclosed herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a schematic diagram of a method for detecting a damage source according to an embodiment of the present invention.
In a first aspect, an embodiment of the present invention provides a method for detecting a damage source, with reference to fig. 1, including seven steps S101 to S107:
S101: and acquiring remote sensing images before and after the damage of the detection area, and identifying house information in the remote sensing images of the detection area before the damage by using a pre-trained house area detection model.
Specifically, in the embodiment of the present invention, the training steps of the pre-trained house area detection model are described in detail below, and the pre-trained house area detection model is used to identify house information in the remote sensing image of the pre-damage detection area，For the serial number of the house in the remote sensing image,For the number of houses in the remote sensing image,The left vertex coordinates of the box are identified for house number i,The width of the box is identified for the ith house,A high of block is identified for house number i.
S102: and converting the remote sensing image into a gray level image, and respectively extracting gray level coordinate sets of house areas in the gray level images before and after the damage of any house.
Specifically, in the embodiment of the invention, the remote sensing image is converted into the gray level image, and gray level coordinate sets of house areas in the gray level images before and after the damage are respectively extracted for any house i，WhereinThe pixel point coordinates of the house area in the gray level image,，。
S103: and determining the m-level degree score before the damage according to the house information in the remote sensing image of the detection area before the damage and the gray scale coordinate set of the house area in the gray scale image before the damage.
Specifically, in the embodiment of the present invention, a method for determining a pre-damage m-level degree score according to house information in a pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in a pre-damage gray scale image is described in detail below.
S104: and determining the post-damage m-level degree score according to the house information in the pre-damage detection area remote sensing image and the gray scale coordinate set of the house area in the post-damage gray scale image.
Specifically, in the embodiment of the present invention, a method for determining a post-damage m-level degree score according to house information in a pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in a post-damage gray scale image is described in detail below.
S105: and determining the damage degree score of each house according to the m-level degree score before damage and the m-level degree score after damage.
Specifically, in the embodiment of the present invention, a method for determining the damage degree score of each house according to the m-level degree score before damage and the m-level degree score after damage is described in detail below.
S106: and determining the coordinates of the center of severe injury, the coordinates of the center of distribution and the coordinates of the center of damage according to the damage degree score of each house and the house information in the remote sensing image of the detection area before damage.
Specifically, in the embodiment of the present invention, a method for determining the coordinates of the serious injury center, the coordinates of the distribution center and the coordinates of the injury center according to the injury degree score of each house and the house information in the remote sensing image of the detection area before injury is described in detail below.
S107: and determining the damage source coordinate according to the serious damage center coordinate, the distribution center coordinate and the damage center coordinate.
Specifically, in the embodiment of the present invention, a method for determining the coordinates of the source of damage according to the coordinates of the center for severe injury, the coordinates of the center for distribution and the coordinates of the center for damage is described in detail below.
Fig. 2 is a schematic diagram II of a method for detecting a damage source according to an embodiment of the present invention.
Further, in the above method for detecting a source of injury, the training step of the pre-trained house area detection model, in combination with fig. 2, includes S201 to S202:
s201: collecting remote sensing images of houses and labeling the houses in the images;
s202: and training the marked house image by using a training model based on the YOLO to obtain a house area detection model.
Further, in the method for detecting a source of damage, the m-level degree score before damage is determined according to the house information in the remote sensing image of the detection area before damage and the gray scale coordinate set of the house area in the gray scale image before damage by the following formula:
wherein, house information in the remote sensing image of the detection area before damage is that ，For the serial number of the house in the remote sensing image,For the number of houses in the remote sensing image,The left vertex coordinates of the box are identified for house number i,The width of the box is identified for the ith house,A high of the block is identified for the ith house,For the pre-lesion m-level degree score,For the pre-lesion m-level degree score,To a gray scale coordinate set of a house area within a pre-lesion gray scale image,The pixel point coordinates of the house area in the gray level image,，。
Further, in the method for detecting a source of damage, the m-level degree score after damage is determined according to the house information in the remote sensing image of the detection area before damage and the gray scale coordinate set of the house area in the gray scale image after damage by the following formula:
wherein, house information in the remote sensing image of the detection area before damage is that ，For the serial number of the house in the remote sensing image,For the number of houses in the remote sensing image,The left vertex coordinates of the box are identified for house number i,The width of the box is identified for the ith house,A high of the block is identified for the ith house,For the m-level degree score after the damage,For the m-level degree score after the damage,In order to obtain the gray scale coordinate set of the house area in the damaged gray scale image,The pixel point coordinates of the house area in the gray level image,，。
Further, in the above method for detecting a source of damage, the damage degree score of each house is determined according to the m degree score before damage and the m degree score after damage by the following formula:
Wherein, For the m-level degree score after the damage,For the pre-lesion m-level degree score,For the score of the extent of the lesion,In order to classify the sub-score,Is the gradation sequence number.
Further, in the above method for detecting a source of damage, the coordinates of the center of severe damage, the coordinates of the center of distribution and the coordinates of the center of damage are determined according to the score of the degree of damage of each house and the information of the house in the remote sensing image of the detection area before damage by the following formula:
Wherein, The weight of the injury judging factor is that,The coordinates of the center of the injury are as follows,In order to obtain the distribution center coordinate as follows,In order to destroy the central coordinate of the device,A damage level score for each house i,To detect house information in the remote sensing image of the area before damage,For the serial number of the house in the remote sensing image,For the number of houses in the remote sensing image,The left vertex coordinates of the box are identified for house number i,The width of the box is identified for the ith house,A high of block is identified for house number i.
Further, in the above method for detecting a source of damage, the source of damage coordinate is determined according to the center coordinates of severe damage, the center coordinates of distribution and the center coordinates of damage by the following formula:
The coordinates of the center of the injury are as follows, In order to obtain the distribution center coordinate as follows,In order to destroy the central coordinate of the device,Is the source coordinate of the injury.
In a second aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory;
the processor is configured to execute a method for detecting a source of injury according to any one of the above by calling a program or instructions stored in the memory.
In a third aspect, embodiments of the present invention further provide a computer-readable storage medium storing a program or instructions that cause a computer to execute a damage source detection method according to any one of the above.
Fig. 3 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.
As shown in fig. 3, the electronic device includes: at least one processor 301, at least one memory 302, and at least one communication interface 303. The various components in the electronic device are coupled together by a bus system 304. A communication interface 303 for information transfer with an external device. It is understood that bus system 304 is used to enable connected communications between these components. The bus system 304 includes a power bus, a control bus, and a status signal bus in addition to the data bus. The various buses are labeled in fig. 3 as bus system 304 for clarity of illustration.
It is to be understood that the memory 302 in this embodiment may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
In some implementations, the memory 302 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system and application programs.
The operating system includes various system programs, such as a framework layer, a core library layer, a driving layer, and the like, and is used for realizing various basic services and processing hardware-based tasks. Applications, including various applications such as a media player (MEDIA PLAYER), browser (Browser), etc., are used to implement various application services. The program for implementing any one of the methods for detecting the source of the injury provided by the embodiment of the invention can be contained in the application program.
In the embodiment of the present invention, the processor 301 is configured to execute the steps of each embodiment of the method for detecting a damage source provided by the embodiment of the present invention by calling a program or an instruction stored in the memory 302, specifically, a program or an instruction stored in an application program.
Acquiring remote sensing images before and after the damage of the detection area, and identifying house information in the remote sensing images of the detection area before the damage by using a pre-trained house area detection model;
converting the remote sensing image into a gray level image, and respectively extracting gray level coordinate sets of house areas in the gray level image before and after the damage of any house;
Determining a pre-damage m-level degree score according to house information in the pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in the pre-damage gray scale image;
Determining a post-damage m-level degree score according to house information in the pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in the post-damage gray scale image;
Determining a damage degree score for each house according to the pre-damage m-level degree score and the post-damage m-level degree score;
Determining a severe injury center coordinate, a distribution center coordinate and a damage center coordinate according to the damage degree score of each house and the house information in the remote sensing image of the detection area before damage;
determining a damage source coordinate according to the severe damage center coordinate, the distribution center coordinate and the damage center coordinate;
Wherein the method comprises the steps of Is the gradation sequence number.
Any one of the methods for detecting a damage source provided in the embodiments of the present invention may be applied to the processor 301, or implemented by the processor 301. The processor 301 may be an integrated circuit chip with signal capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry of hardware in the processor 301 or instructions in the form of software. The Processor 301 may be a general purpose Processor, a digital signal Processor (DIGITAL SIGNAL Processor, DSP), an Application SPECIFIC INTEGRATED Circuit (ASIC), an off-the-shelf programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of any one of the methods for detecting the damage source provided by the embodiment of the invention can be directly embodied as the execution completion of the hardware decoding processor, or the execution completion of the combination execution of the hardware and the software units in the decoding processor. The software elements may be located in a random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 302, and the processor 301 reads the information in the memory 302, and in combination with its hardware, performs the steps of a method for detecting the source of a damage.
Those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.
Those skilled in the art will appreciate that the descriptions of the various embodiments are each focused on, and that portions of one embodiment that are not described in detail may be referred to as related descriptions of other embodiments.
The present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. A method for detecting a source of injury, comprising:
acquiring remote sensing images before and after the damage of the detection area, and identifying house information in the remote sensing images of the detection area before the damage by using a pre-trained house area detection model;
converting the remote sensing image into a gray level image, and respectively extracting gray level coordinate sets of house areas in the gray level image before and after the damage of any house;
Determining a pre-damage m-level degree score according to house information in the pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in the pre-damage gray scale image;
Determining a post-damage m-level degree score according to house information in the pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in the post-damage gray scale image;
Determining a damage degree score for each house according to the pre-damage m-level degree score and the post-damage m-level degree score;
Determining a severe injury center coordinate, a distribution center coordinate and a damage center coordinate according to the damage degree score of each house and the house information in the remote sensing image of the detection area before damage;
determining a damage source coordinate according to the severe damage center coordinate, the distribution center coordinate and the damage center coordinate;
Wherein the method comprises the steps of The gray scale is a gray scale grading serial number;
The m-level degree score before damage is determined according to the house information in the remote sensing image of the detection area before damage and the gray scale coordinate set of the house area in the gray scale image before damage, and is determined by the following formula:
；
；
wherein, house information in the remote sensing image of the detection area before damage is that ，/>For the serial number of houses in the remote sensing image,/>For the number of houses in the remote sensing image,/>Left vertex coordinates of the box are identified for house number i,/>Identifying the width of the box for house number i,/>Identify the block high for house number i,/>To score m-level degree before damage,/>To score m-level degree before damage,/>For the gray scale coordinate set of house area in the gray scale image before damage,/>Pixel point coordinates of house area in gray level image,/>，/>；
Determining post-damage m-level degree scores according to house information in the pre-damage detection area remote sensing image and a gray scale coordinate set of a house area in the post-damage gray scale image is determined by the following formula:
；
；
wherein, house information in the remote sensing image of the detection area before damage is that ，/>For the serial number of houses in the remote sensing image,/>For the number of houses in the remote sensing image,/>Left vertex coordinates of the box are identified for house number i,/>Identifying the width of the box for house number i,/>Identify the block high for house number i,/>To score m-level degree after damage,/>To score m-level degree after damage,/>For the gray scale coordinate set of house area in the damaged gray scale image,/>Pixel point coordinates of house area in gray level image,/>，/>。
2. A method of lesion source detection according to claim 1, wherein the training step of the pre-trained house area detection model comprises:
collecting remote sensing images of houses and labeling the houses in the images;
And training the marked house image by using a training model based on the YOLO to obtain a house area detection model.
3. A method of detecting a source of injury according to claim 1 wherein determining a degree of injury score for each house based on the pre-injury m-level degree score and the post-injury m-level degree score is determined by the following formula:
；
；
Wherein, To score m-level degree after damage,/>To score m-level degree before damage,/>For the score of the extent of the lesion,To classify sub-scores,/>Is the gradation sequence number.
4. A method of detecting a source of injury according to claim 3 wherein the determination of the center coordinates of severe injury, the center coordinates of distribution and the center coordinates of injury from the score of the extent of injury of each house and the information of the house in the remote sensing image of the pre-injury detection area is determined by the following formula:
；
；
；
；
；
；
；
Wherein, Judging factor for severe injury,/>The center coordinates of the severe injury are,/>In order to obtain the distribution center coordinate as follows,The center coordinate of the damage is/(Score for the extent of damage of each house i,/>For house information in remote sensing image of detection area before damage,/>For the serial number of houses in the remote sensing image,/>For the number of houses in the remote sensing image,/>Left vertex coordinates of the box are identified for house number i,/>The width of the box is identified for the ith house,A high of block is identified for house number i.
5. The method of claim 4, wherein determining the source coordinates from the center coordinates of the severe injury, the center coordinates of the distribution, and the center coordinates of the damage is determined by the following formula:
；
；
Center coordinates of severe injury,/> For distributing center coordinates,/>To destroy center coordinates,/>Is the source coordinate of the injury.
6. An electronic device, comprising: a processor and a memory;
the processor is configured to execute a method for detecting a source of injury according to any one of claims 1 to 5 by calling a program or instructions stored in the memory.
7. A computer-readable storage medium storing a program or instructions that cause a computer to perform a method of detecting a source of injury as claimed in any one of claims 1 to 5.
Priority Applications (1)
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| JP2017096867A (en) * | 2015-11-27 | 2017-06-01 | 株式会社熊谷組 | Building damage determination method |
| KR101793264B1 (en) * | 2017-09-08 | 2017-11-02 | 조현석 | Analysis method for occurrence and growth progression of crack |
| JP2020030730A (en) * | 2018-08-24 | 2020-02-27 | 朝日航洋株式会社 | House movement reading system, house movement reading method, house movement reading program, and house loss reading model |
| WO2023167017A1 (en) * | 2022-03-04 | 2023-09-07 | 富士フイルム株式会社 | House state provision device and method |
| JP2023129966A (en) * | 2022-03-07 | 2023-09-20 | 株式会社フジタ | Displacement measurement device and damage degree evaluation system |
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| JP7387911B2 (en) * | 2020-09-18 | 2023-11-28 | 富士フイルム株式会社 | Flood damage assessment device, flood damage assessment method and program |
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| JP2016197013A (en) * | 2015-04-02 | 2016-11-24 | アズビル株式会社 | Building damage intensity estimating system and method |
| JP2017096867A (en) * | 2015-11-27 | 2017-06-01 | 株式会社熊谷組 | Building damage determination method |
| KR101793264B1 (en) * | 2017-09-08 | 2017-11-02 | 조현석 | Analysis method for occurrence and growth progression of crack |
| JP2020030730A (en) * | 2018-08-24 | 2020-02-27 | 朝日航洋株式会社 | House movement reading system, house movement reading method, house movement reading program, and house loss reading model |
| WO2023167017A1 (en) * | 2022-03-04 | 2023-09-07 | 富士フイルム株式会社 | House state provision device and method |
| JP2023129966A (en) * | 2022-03-07 | 2023-09-20 | 株式会社フジタ | Displacement measurement device and damage degree evaluation system |
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