Helmet wear detection based on YOLOV5
Abstract
Safety helmet wearing detection is an important safety inspection task with widespread applications in industries, construction, and transportation. Traditional safety helmet wearing detection methods typically use feature-based classifiers such as SVM and decision trees, but these methods often have low accuracy and poor adaptability. In this paper, we propose an improved helmet detection method that uses a combination of SPD Conv, ASPP and BiFPN structures to increase the perceptual field to ensure maximum feature extraction from the helmet, and can ensure fusion between different feature layers to pass semantic information to deeper neural networks, effectively avoiding information loss and improving the performance of detecting helmets. Experimental results show that our method has a 1% improvement in the average accuracy of detection in the public dataset VCO2007 set compared to YOLOv5, which still allows for real-time detection and meets the needs of industry with some practicality.
References
[1]
Redmon J, Divvala S, Girshick R, You only look once: Unified, real-time object detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.
[2]
Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271
[3]
Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018, arXiv:1804.02767.
[4]
Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020.
[5]
Ultralytics-Yolov5. Available online: https://github.com/ultralytics/yolov5 (accessed on 5 June 2022).
[6]
Tian Z, Shen C, Chen H, Fcos: Fully convolutional one-stage object detection[C]. Proceedings of the IEEE/CVF international conference on computer vision. 2019: 9627-9636.
[7]
Tan, M.; Pang, R.; Le, Q.V. Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June; pp. 10781–10790.
[8]
LI, Yiting, Research on a surface defect detection algorithm based on MobileNet-SSD. Applied Sciences, 2018, 8.9: 1678.
[9]
CHENG, Rao, Multi-scale safety helmet detection based on SAS-YOLOv3-tiny. Applied Sciences, 2021, 11.8: 3652.
[10]
WARANUSAST, Rattapoom, Machine vision techniques for motorcycle safety helmet detection. In: 2013 28th International conference on image and vision computing New Zealand (IVCNZ 2013). IEEE, 2013. p. 35-40.
[11]
LI, Kang, Automatic safety helmet wearing detection. arXiv preprint arXiv:1802.00264, 2018.
[12]
DASGUPTA, Madhuchhanda; BANDYOPADHYAY, Oishila; CHATTERJI, Sanjay. Automated helmet detection for multiple motorcycle riders using CNN. In: 2019 IEEE Conference on Information and Communication Technology. IEEE, 2019. p. 1-4.
[13]
ROHITH, C. A., An efficient helmet detection for MVD using deep learning. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2019. p. 282-286.
[14]
DENG, Lixia, A lightweight YOLOv3 algorithm used for safety helmet detection. Scientific reports, 2022, 12.1: 10981.
[15]
HUANG, Li, Detection algorithm of safety helmet wearing based on deep learning. Concurrency and Computation: Practice and Experience, 2021, 33.13: e6234.
[16]
MOHAMMADI, Esmaeil, Barriers and factors associated with the use of helmets by Motorcyclists: A scoping review. Accident Analysis & Prevention, 2022, 171: 106667.
[17]
LIU, Weiyang, Large-margin softmax loss for convolutional neural networks. arXiv preprint arXiv:1612.02295, 2016.
[18]
XU, Zhanwei; WU, Ziyi; FENG, Jianjiang. CFUN: Combining faster R-CNN and U-net network for efficient whole heart segmentation. arXiv preprint arXiv:1812.04914, 2018.
[19]
JIANG, Zicong, Real-time object detection method based on improved YOLOv4-tiny. arXiv preprint arXiv:2011.04244, 2020.
[20]
WANG, Rui; SHI, Yijie; CAO, Wenming. GA-SURF: A new speeded-up robust feature extraction algorithm for multispectral images based on geometric algebra. Pattern Recognition Letters, 2019, 127: 11-17.
[21]
LIN, Tsung-Yi, Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. p. 2117-2125.
Index terms have been assigned to the content through auto-classification.
Recommendations
Small-scale helmet detection based on improved YOLOv5 and moving object detection
SUI '23: Proceedings of the 2023 ACM Symposium on Spatial User InteractionIn industrial human-computer interaction, the safety of human is particularly important, helmet wearing plays a crucial role in ensuring construction safety and security. In smart industrial systems, intelligent helmet detection is a fundamental task. ...
Safety Helmet Wearing Detection Based on Improved YOLOv5s
ICIGP '23: Proceedings of the 2023 6th International Conference on Image and Graphics ProcessingFor construction sites in high-risk industries such as the construction industry, wearing a helmet can minimize head injuries. Aiming at the low detection accuracy of the existing detection algorithms for wearing helmets, and the detection of small ...
Safety Helmet Detection Based on YOLOv7
CSAE '22: Proceedings of the 6th International Conference on Computer Science and Application EngineeringFrequent safety accidents have posed a significant risk to workers' lives recently. In particular, the risk of head injury is significantly increased by workers not wearing helmets. However, manual supervision is inefficient and costly. Even though ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
Acceptance Rates
CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 55Total Downloads
- Downloads (Last 12 months)28
- Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format