Object detection for helmet wearing situation during the process of construction

Yingqi Bai; Jingguo Lv; Chen Wang

doi:10.1117/12.2624703

13 December 2021 Object detection for helmet wearing situation during the process of construction

Yingqi Bai, Jingguo Lv, Chen Wang

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 1208729 (2021) https://doi.org/10.1117/12.2624703
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

In the process of construction, it is not uncommon for workers to enter the construction area without wearing safety helmet correctly. In order to reduce the occurrence of such accidents, this paper uses the target detection algorithm to quickly check the situation of construction workers wearing safety helmets. Firstly, the Stochastic Gradient Descent algorithm is used to improve the speed of target detection. Secondly, for different sizes of helmets, the receptive field is increased by dilated convolution to achieve multi-scale target detection. Thirdly, in order to improve the positioning and classification accuracy of the model, the feature fusion method of skip connection is used to improve the feature extraction ability of the network. Finally, the identity of construction personnel is determined according to the color of safety helmet to improve the expression ability of detection results. The experimental results show that the detection speed of this algorithm reaches 32fps, and the MAP reaches 88%, which can meet the detection requirements in the actual engineering scene. At the same time, the matching of helmet type and worker identity is realized.

Citation Download Citation

Yingqi Bai, Jingguo Lv, and Chen Wang "Object detection for helmet wearing situation during the process of construction", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 1208729 (13 December 2021); https://doi.org/10.1117/12.2624703

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