Safety identification in underground high-voltage scene based on lightweight YOLOv7
In order to enable the safety monitoring platform of Yangliu Coal Mine to accurately and quickly identify the unsafe behaviors in the high-voltage operation scene of electromechanical personnel,taking the underground central substation as an example,the safety identification framework was designed by focusing on the wearing of insulation protectors.Based on the YOLOv7 target detection algorithm,the partial convolution(PConv)was used to improve the robustness and generalization ability of the model in dealing with occluded or missing images.The fast neural network structure(FasterNet)was fused to reduce the computational redundancy and optimize the detection performance.Finally,the convolutional block attention module(CBAM)was fused to improve the feature extraction ability of the algorithm.The experimental results showed that,the volume of the lightweight model was reduced by 30.5%compared with the original model,the calculation amount was reduced by 23.7%,the average recognition accuracy was up to 97.3%,and the detection speed of a single image was increased by 38.1%.The problem of missed detection was effectively solved in the small target detection task under complex background.
underground high voltage operationcoal mine electromechanical personnelYOLOv7-tinyinsulation protectorpartial convolution
NETL
NSTL
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