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Yu et al., 2021 - Google Patents

A real-time detection approach for bridge cracks based on YOLOv4-FPM

Yu et al., 2021

Document ID
4258027072543208797
Author
Yu Z
Shen Y
Shen C
Publication year
Publication venue
Automation in Construction

External Links

Snippet

In order to realize real-time detection for bridge cracks by unmanned aerial vehicle (UAV), a deep learning model named YOLOv4-FPM is proposed on the basis of the YOLOv4 model. In YOLOv4-FPM, focal loss is used to optimize the loss function, which improves the …
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Classifications

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    • G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
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    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
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    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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