Research on safety clothing detection method for surveillance video of thermal power plant

doi:10.11996/JG.j.2095-302X.2023020291

Abstract

Abstract:

In the construction of a smart power plant, computer vision technology is usually utilized to detect the surveillance video returned by surveillance cameras deployed in industrial plants to monitor whether workers are wearing safety clothing. However, due to the complexity of the scenes in the thermal power plant, the existing datasets and algorithms proved inadequate in achieving the desired level of accuracy. Based on the industrial surveillance video, a dataset for safety clothing target detection in the thermal power plant scene was constructed. To address the problem of YOLOv5 being insufficient in the detection accuracy for safety clothing targets, various algorithm models such as EfficientNet, ResNet-50, ShuffleNet, and MobileNet were implemented as alternatives to the original YOLOv5 Backbone module network structure. Additionally, model fusion algorithms based on YOLOv5 were proposed. Based on the industrial scene safety clothing target detection dataset, the optimal algorithms in related fields were selected and compared with the optimized YOLOv5 algorithm. The experimental results demonstrated that the accuracy of the improved YOLOv5+EfficientNet algorithm in the industrial scene security service dataset has been significantly improved, with the highest detection accuracy of 96.6%.

Key words: thermal power plant, surveillance video, safety clothing, object detection, YOLOv5

CLC Number:

TP391

CHEN Gang, ZHANG Pei-ji, GONG Dong-dong, YU Jun-qing. Research on safety clothing detection method for surveillance video of thermal power plant[J]. Journal of Graphics, 2023, 44(2): 291-297.

Figures/Tables 11

References 13

[1]	HU J L, LIN X B, LI C H, et al. Protective clothing detection of substation workers using S-HOG+C operator[C]// 2018 International Conference on Power System Technology. New York: IEEE Press, 2019: 4181-4185.
[2]	WANG X Y, NIU D, LUO P X, et al. A safety helmet and protective clothing detection method based on improved-yolo V₃[C]// 2020 Chinese Automation Congress. New York: IEEE Press, 2021: 5437-5441.
[3]	陈双, 何利力. 基于Faster R-CNN的服装目标检测改进方法[J]. 软件导刊, 2020, 19(4): 42-45.
	CHEN S, HE L L. Improved method for garment target detection based on faster R-CNN[J]. Software Guide, 2020, 19(4): 42-45. (in Chinese)
[4]	DE SOUZA INÁCIO A, LOPES H S. EPYNET: efficient pyramidal network for clothing segmentation[J]. IEEE Access, 2020, 8: 187882-187892. DOI URL
[5]	LEE C H, LIN C W. A two-phase fashion apparel detection method based on YOLOv4[J]. Applied Sciences, 2021, 11(9): 3782. DOI URL
[6]	WANG Z J, WU Y M, YANG L C, et al. Fast personal protective equipment detection for real construction sites using deep learning approaches[J]. Sensors: Basel, Switzerland, 2021, 21(10): 3478.
[7]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. DOI URL
[8]	SAUDI M M, HAKIM A, AHMAD A, et al. Image detection model for construction worker safety conditions using faster R-CNN[J]. International Journal of Advanced Computer Science and Applications, 2020, 11(6): 246.
[9]	HUMPHREY E J, BELLO J P. Rethinking automatic chord recognition with convolutional neural networks[C]// The 11th International Conference on Machine Learning and Applications. New York: IEEE Press, 2013: 357-362.
[10]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// The IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2016: 770-778.
[11]	MA N N, ZHANG X Y, ZHENG H T, et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[C]// Computer Vision - ECCV 2018:15th European Conference. New York: ACM, 2018: 122-138.
[12]	HOWARD A, SANDLER M, CHEN B, et al. Searching for MobileNetV3[C]// 2019 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2020: 1314-1324.
[13]	ZHONG Z, ZHENG L, KANG G L, et al. Random erasing data augmentation[C]// The AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2017: 1-11.

Method	Backbone	P	R	mAP@.5	mAP@.5:.95
文献[2]	Darknet-53	0.883	0.905	0.880	-
文献[3]	VGG16	-	-	0.861	-
文献[3]	ResNet-101	-	-	0.858	-
文献[5]	CSPDarknet-53	0.872	0.896	0.907	-
YOLOv5	CSPDarknet-53	0.886	0.908	0.929	0.701
YOLOv5	ResNet-50	0.911	0.859	0.935	0.671
YOLOv5	ShuffleNet V2	0.911	0.852	0.921	0.639
YOLOv5	MobileNet V3	0.904	0.855	0.933	0.657
YOLOv5	EfficientNet	0.910	0.879	0.951	0.707

Method	Backbone	P	R	mAP@.5	mAP@.5:.95
文献[2]	Darknet-53	0.883	0.905	0.880	-
文献[3]	VGG16	-	-	0.861	-
文献[3]	ResNet-101	-	-	0.858	-
文献[5]	CSPDarknet-53	0.872	0.896	0.907	-
YOLOv5	CSPDarknet-53	0.886	0.908	0.929	0.701
YOLOv5	ResNet-50	0.911	0.859	0.935	0.671
YOLOv5	ShuffleNet V2	0.911	0.852	0.921	0.639
YOLOv5	MobileNet V3	0.904	0.855	0.933	0.657
YOLOv5	EfficientNet	0.910	0.879	0.951	0.707

Method	Backbone	P	R	mAP@.5	mAP@.5:.95
文献[2]	Darknet-53	0.881	0.933	0.908	-
文献[5]	CSPDarknet-53	0.890	0.923	0.935	-
YOLOv5	CSPDarknet-53	0.900	0.935	0.919	0.698
YOLOv5	ResNet-50	0.907	0.928	0.962	0.748
YOLOv5	ShuffleNet V2	0.911	0.917	0.960	0.723
YOLOv5	MobileNet V3	0.925	0.914	0.963	0.722
YOLOv5	EfficientNet B8	0.944	0.915	0.966	0.761

Method	Backbone	P	R	mAP@.5	mAP@.5:.95
文献[2]	Darknet-53	0.881	0.933	0.908	-
文献[5]	CSPDarknet-53	0.890	0.923	0.935	-
YOLOv5	CSPDarknet-53	0.900	0.935	0.919	0.698
YOLOv5	ResNet-50	0.907	0.928	0.962	0.748
YOLOv5	ShuffleNet V2	0.911	0.917	0.960	0.723
YOLOv5	MobileNet V3	0.925	0.914	0.963	0.722
YOLOv5	EfficientNet B8	0.944	0.915	0.966	0.761

Method	Backbone	P	R	mAP@.5	mAP@.5:.95
文献[2]	Darknet-53	0.791	0.912	0.899	-
文献[3]	VGG16	-	-	0.915	-
文献[3]	ResNet-101	-	-	0.904	-
文献[5]	CSPDarknet-53	0.918	0.939	0.949	-
YOLOv5	CSPDarknet-53	0.937	0.951	0.965	0.734
YOLOv5	ResNet-50	0.924	0.954	0.969	0.701
YOLOv5	ShuffleNet V2	0.954	0.933	0.971	0.781
YOLOv5	MobileNet V3	0.936	0.921	0.964	0.788
YOLOv5	EfficientNet B8	0.933	0.945	0.966	0.815