Research on image recognition technology for external scaffold hidden danger based on improved YOLOv5s

doi:10.16265/j.cnki.issn1003-3033.2023.12.2011

Abstract

Abstract:

In order to improve the quality and efficiency of external scaffold safety management, an improved YOLOv5 s external scaffold hidden danger identification method is proposed based on image recognition technology.Firstly, in order to solve the problem that the recognition precision decreases due to more background information, a convolutional block attention module(CBAM) was embedded in the algorithm backbone network to obtain various detailed features of hidden danger. Secondly, the neck feature fusion module was improved into BiFPN, which can effectively deal with the problem of multi-scale feature imbalance caused by the uneven size distribution of scaffolding hidden targets. Thirdly, the original frame loss function was replaced by Scylla intersection over union(SIoU) Loss. Finally, the influence of the improved module on the performance of the model was analyzed by ablation test and compared with other algorithms to verify the recognition effect. The results show that the score of mAP@0.5:0.95 is increased by 5.13%, and the recall rate is increased by 3.45 %. The algorithm has a good recognition effect in multi-scale, the multi-target and complex background of construction sites. It provides technical support for the further application of image recognition technology in the field of external scaffold safety management in a construction site.

Key words: YOLOv5s, external scaffold hidden danger, image recognition, multi-scale features, mean average precision(mAP), bi-directional feature pyramid network(BiFPN)

ZHAO Jiangping, LIU Xingxing, ZHANG Xiangzhuo. Research on image recognition technology for external scaffold hidden danger based on improved YOLOv5s[J]. China Safety Science Journal, 2023, 33(12): 60-66.

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标签类别	标签数量	备注
密目网	782	防护网未密封类隐患
剪刀撑	544	剪刀撑不规范搭设类隐患
异物	1 297	架体集中荷载有异物类隐患

参数	数值	参数	数值
初始学习率	0.001	权重衰减系数	0.000 5
迭代批次	16	训练轮次	100
非极大抑制阈值	0.3	置信度阈值	0.5

模型	注意力模块		SIoU Loss	BiFPN	P/%	R/%	mAP/%		参数量/ 10⁶
模型	CBAM	SE	SIoU Loss	BiFPN	P/%	R/%	mAP@.5	mAP@.5:.95	参数量/ 10⁶
YOLOv5s	—	—	—	—	91.00	89.43	92.66	55.72	7.06
A	√	—	—	—	93.45	89.67	94.05	58.55	7.11
B	—	√	—	—	90.10	89.0	92.14	55.22	7.59
C	—	—	√	—	93.46	91.11	94.81	58.93	7.06
D	—	—	—	√	92.17	89.81	93.54	56.13	8.13
E	√		√	—	92.10	92.52	93.20	58.98	7.11
F	√	—	—	√	90.97	92.14	94.42	58.18	8.18
G	—	—	√	√	90.05	92.82	93.88	56.55	8.13
YOLOv5_SW	√	—	√	√	92.90 (+1.9%)	92.88 (+3.45%)	94.83 (+2.17%)	60.85 (+5.13%)	8.18 (+1.12%)

网络模型	P/%	R/%	mAP@.5/%	FPS/(帧·s^-1)	参数量/10⁶	浮点运算数/10⁹
YOLOv5s	91.00	89.43	92.66	97.0	7.06	16.500
YOLOv4	90.80	89.62	89.61	47.9	63.95	29.983
YOLOv3	91.60	85.67	88.45	71.7	61.54	32.806
SSD-VGG16	88.31	79.14	88.31	143.0	26.29	30.477
Faster R-CNN-VGG16	69.99	92.62	90.37	29.5	136.73	184.884
YOLOv5_SW	92.90	92.88	94.83	83.3	8.18	17.900