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.
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Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 29 May 2023
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CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
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CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
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