Improved Helmet Wearing Detection Method Based on YOLOv3

Wen, Peng; Tong, Mingyang; Deng, Zhenrong; Qin, Qinzhi; Lan, Rushi

doi:10.1007/978-3-030-57884-8_59

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…It has the ability to process images very fast while containing a high accuracy rate. Wen et al (2020) conducted a comparative study in which they evaluated an improved YOLOv3 network against YOLOv2 and the standard YOLOv3 for safety helmet detection. Their findings revealed that the improved YOLOv3 model attained a precision of 90.7%, surpassing precisions achieved by YOLOv2 and the conventional YOLOv3.…”

Section: Literature Reviewmentioning

confidence: 99%

Hard hat detection for safety purposes by using YOLOv9

Perišić,

Jovanović

2024

XIX International May Conference on Strategic Management – IMCSM24 Proceedings - Zbornik Radova

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Ensuring the safety of workers at workplaces is a crucial task for every company. The usage of personal protective equipment represents the basic form of protection. Hard hats are very useful in protecting head from injuries. However, workers often neglect the importance of wearing safety helmets and do not wear them. Systems for monitoring and detecting unsafe behaviors can be very helpful for maintaining security. For that purpose, this research examines the success of the application of the latest YOLO algorithm for detecting the presence of safety helmets on workers that can be applied in those systems. Two models with different numbers of parameters are trained for this purpose-YOLOv9c and YOLOv9e. The results showed that YOLOv9c model achieved mean average precision of 97.2%, 93%, and 92.9% in training, validation, and testing, respectively, while YOLOv9e reached slightly higher mean average precisions of 97.5% in training, 93.4% in validation and 93.4% in testing.

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Section: Literature Reviewmentioning

confidence: 99%

Hard hat detection for safety purposes by using YOLOv9

Perišić,

Jovanović

2024

XIX International May Conference on Strategic Management – IMCSM24 Proceedings - Zbornik Radova

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“…Wang et al enhanced the representation ability of target features by introducing convolutional block attention modules in the neck to assign weights and weaken feature extraction from complex backgrounds [17]. Wen et al used the soft-NMS algorithm to optimize the YOLOv3 model, and the improved YOLOv3 algorithm was able to effectively detect occluded targets, but the target detection was not satisfactory when the occlusion rate exceeded 60% [18]. Wang et al proposed an improved helmet wear detection algorithm, YOLOv4-P, which improves the accuracy of helmet wear detection by increasing the mAP value by 2.15% compared to the YOLOv4 algorithm [19].…”

Section: Introductionmentioning

confidence: 99%

Detection of Safety Helmet-Wearing Based on the YOLO_CA Model

Wu,

Qian,

Yang

2023

CMC

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Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents, as well as being of great significance to construction safety. However, for a variety of reasons, construction workers nowadays may not strictly enforce the rules of wearing safety helmets. In order to strengthen the safety of construction site, the traditional practice is to manage it through methods such as regular inspections by safety officers, but the cost is high and the effect is poor. With the popularization and application of construction site video monitoring, manual video monitoring has been realized for management, but the monitors need to be on duty at all times, and thus are prone to negligence. Therefore, this study establishes a lightweight model YOLO_CA based on YOLOv5 for the automatic detection of construction workers' helmet wearing, which overcomes the shortcomings of the current manual monitoring methods that are inefficient and expensive. The coordinate attention (CA) addition to the YOLOv5 backbone strengthens detection accuracy in complex scenes by extracting critical information and suppressing noncritical information. Further parameter compression with deeply separable convolution (DWConv). In addition, to improve the feature representation speed, we swap out C3 with a Ghost module, which decreases the floating-point operations needed for feature channel fusion, and CIOU_Loss was substituted with EIOU_Loss to enhance the algorithm's localization accuracy. Therefore, the original model needs to be improved so as to enhance the detection of safety helmets. The experimental results show that the YOLO_CA model achieves good results in all indicators compared with the mainstream model. Compared with the original model, the mAP value of the optimized model increased by 1.13%, GFLOPs cut down by 17.5%, and there is a 6.84% decrease in the total model parameters, furthermore, the weight size cuts down by 4.26%, FPS increased by 39.58%, and the detection effect and model size of this model can meet the requirements of lightweight embedding.

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