Computer vision based safety inspection of high abutting edges

doi:10.16265/j.cnki.issn1003-3033.2023.09.0202

Abstract

Abstract:

To address the issues of high danger, low efficiency, and complex recognition scenarios in the safety inspection work of construction site managers, a new detection method for high-altitude safety protection equipment was proposed using convolutional neural network (CNN) technology. This method combines computer vision to detect the wearing status of personal protective equipment such as safety helmets and harnesses, as well as any damage to protective nets. Additionally, based on the YOLOv5 algorithm, the attention model was modified and a lightweight detection software was developed. The results indicate that after lightweighting, the model size decreased to 1.9 MB, a reduction of 86.8% compared to before the modification. Under graphics processing unit(GPU) operating conditions, the single-frame image detection time was optimized to 40-50 ms, representing a reduction of 65%-80% compared to before, greatly improving the detection speed.

Key words: computer vision, elevated proximity, safety inspection, lightweight, construction site, YOLOv5

LI Hua, WU Lizhou, XUE Xicheng, ZHONG Xingrun. Computer vision based safety inspection of high abutting edges[J]. China Safety Science Journal, 2023, 33(9): 69-75.

Figures/Tables 9

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类别	解释
TP(True Positives)	实际为正例且被分类器划分为正例的实例数
FP(False Positives)	实际为负例但被分类器划分为正例的实例数
FN(False Negatives)	实际为正例但被分类器划分为负例的实例数
TN(True Negatives)	实际为负例且被分类器划分为负例的实例数

训练方法	P	R	mAP@ 0.5	mAP@ 0.5:0.95	模型内存/ MB	运算时间/ h
YOLOv5	0.77	0.73	0.74	0.41	14.4	6.89
YOLOv5-SE	0.82	0.70	0.72	0.40	15.4	7.00
YOLOv5-CBAM	0.83	0.68	0.73	0.40	14.7	6.96
YOLOv5-ECA	0.84	0.70	0.73	0.40	14.7	7.04
YOLOv5-CA	0.83	0.68	0.71	0.40	14.4	7.23

训练方法	P	模型内存/MB	运算时间/h
YOLOv5	0.77	14.4	6.886
YOLOv5-ECA	0.84	14.7	7.038
YOLOv5-ShuffleNerV2	0.83	1.9	6.435

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