Abstract
Abstract In foggy weather, inspection robots detect low visibility and unclear targets in substation power equipment, and the commonly used target detection algorithms have too many parameters, making it difficult to achieve real-time requirements when deployed on inspection robots. To this end, a lightweight substation power equipment identification method combining Cycle Dehaze defogging algorithm is proposed. Pre process the collected fog map of the substation to improve the clarity and visibility of the target power equipment; Introducing MobileNetv3 and deep separable convolution in YOLOv4 network to achieve model lightweight; To increase the precision of power equipment identification, incorporate the SE attention mechanism into the target detection network. Based on the testing results, it is possible to reach a recognition mAP of 92.5% by combining Cycle Dehaze and improved YOLOv4, as the improved YOLOv4 has a detection speed that is 15.59 frames/second faster than the YOLOv4 method. The model parameter quantity has been reduced by 80.21%, which is more conducive to deployment on substation inspection robots.
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