Research on Smart Grid Violation Recognition Algorithm Based on Deep Learning
Aiming at the problem of low detection accuracy in complex scenes such as dense detection targets and large scale differences in safety helmet detection in smart grid infrastructure construction site violation identification,an improved safety helmet wearing detection method based on YOLOv7 was proposed.Firstly the multi-scale coordinate attention mechanism(MCA)was added to the backbone of model,it improved the ability to recognize the small objects with more feature informa-tion under different receptive field.Secondly,by using the circular characteristics of the detection target,BCR was designed to replace BBR to learn the target,so as to reduce the interference of image background information and improve the recognition rate of dense targets.A dynamic Focal-cWIoU loss function based on cIoU was proposed to dynamically adjust the geometric penalty term,reduce the influence of low-quality samples and improve the detection accuracy of the model.The test results showed that the detection time and accuracy of this method could meet the requirements of various complex construction scenes in power infrastructure site.
power grid infrastructure constructionsafety helmet detectionYOLOv7focal circle wise intersection over union(Focal-cWIoU)multi-scale coordinate attention mechanism
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