Substation High-Voltage Switchgear Detection Based on Improved EfficientNet-YOLOv5s Model

Aiming at the current problems of low detection accuracy of high-voltage cabinet switches and large models that are difficult to deploy, a high-voltage cabinet switch detection method based on the improved EfficientNet-YOLOv5s model is proposed. Firstly, offline data expansion and online Mosaic data enhancement are performed based on the high-voltage switchgear images obtained by video frame extraction to alleviate the problems of unbalanced sample categories and the small number of samples. Secondly, the backbone network of the YOLOv5s model is replaced with a lightweight EfficientNet-B0 network, and the original convolution layer is replaced by depthwise separable convolution, and the original Squeeze-and-Excitation Networks were replaced with Efficient Channel Attention to optimize the efficiency and accuracy of the network model. To enhance the positioning information of small objects without bringing too much computational burden, the proposed improved multi-scale feature fusion Neck network uses the Bi-directional Concatenation(BiC) module to aggregate the feature maps of three adjacent layers. Finally, the H-Swish function is used to replace the Swish function in the MBConv structure, avoiding the exponential operation of the sigmoid function, and further improving the calculation efficiency of the model.