电力巡检中改进YOLOv5s的缺陷检测算法研究OA北大核心CSTPCD

A modified defect detection algorithm based on YOLOv5s is proposed to address the issue of low detection accuracy for critical components during power line inspections using drones.The algorithm introduces a convolutional neural network attention module(CBAM)in the backbone network to enhance the efficiency of extracting important information from feature maps.The original PANet feature fusion framework in YOLOv5s is replaced with a bidirectional feature pyramid network(BiFPN),which incorporates learnable weights to map different feature contributions,thereby increasing the importance of significant feature mappings.Additionally,a context convolution module is added on top of the spatial pyramid pooling(SPP)module to improve feature representation capabilities.Experimental verification is conducted by using aerial photography datasets,demonstrating that the improved algorithm achieves an mAP of 95.6%,accuracy of 93.7%,and recall rate of 93.8%.To further validate the algorithm's performance on embedded systems,the model is accelerated and deployed on the Jetson Xavier NX platform,the average runtime for a single-frame image is 24.6 ms with a detection accuracy of 90.8%and a recall rate of 90.5%.This capability allows for precise object recogni-tion on Jetson Xavier NX devices.The improved model has enhanced detection accuracy,demonstrating the effectiveness of the algorithm and meeting real-time detection requirements for power line inspections.