Citation

Jiangping Z, Xingxing LIU, Xiangzhuo Z. China Saf. Sci. J. 2023; 33(12): 60-66.

Copyright

(Copyright © 2023, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2023.12.2011

PMID

unavailable

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)

Language: en