Detection of Safety Protection Equipment in Coal Mine Substations Based on DETR-SGC Algorithm
YANG Wenke
WANG Xiangqian
Abstract：In order to intelligently monitor the wearing of protective equipment of personnel in underground coal mine substations,and avoid the problem of uneven lighting,dust interference,obstruction,and other factors affecting the precision of video detection in monitoring videos,a detection Transformer smooth-L1 ghost convolution(DETR-SGC)algorithm was proposed for the detection of safety protective equipment in coal mine substations.Firstly,in the position encoding part of the detection Transformer(DETR)algorithm,a ghost batchnormalization sigmoid gated linear unit-squeeze and excitation(GBS-SE)module was introduced to enhance the spatial feature extraction capability of the algorithm.Secondly,the convolutional block attention module(CBAM)was integrated into the Transformer module to improve the extraction of channel and spatial dimension features,thereby enhancing the detection precision of the algorithm.Finally,the fusion of smooth-L,norm loss and generalized intersection over union(GIoU)loss functions improved the regression accuracy of the algorithm.The experiments showed that the average precision,recall rate,and mean average precision of the DETR-SGC algorithm had reached 93.3％,87.9％,and 91.3％,respectively,which were 10.8％,4.3％,and 5.9％higher than the original DETR algorithm.Therefore,the algorithm can effectively solve the detection problem of safety protection equipment worn by personnel in coal mine substations.
Keywords：detection of safety protection equipmentDETR-SGCTransformerCBAMsmooth-L1GIoU loss function
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 528-532,581 )