Multi-Objective Defect Detection Method for Transmission Lines Based on Improved YOLOv8

Aiming at the problem that the traditional detection method has low efficiency and accuracy due to the fact that there are many key component targets to be inspected by unmanned aerial vehicle (UAV) during power inspection, and the shape difference is large, and the image quality is not high, a transmission line abnormal target detection method based on improved YOLOv8 and super-resolution reconstruction is proposed. Firstly, the super-resolution reconstruction algorithm is used to reconstruct the abnormal image to improve the clarity and enrich the characteristic information contained in the image. On this basis, the improved YOLOv8 network is used to detect the defects in the inspection image. The CBAM attention mechanism is fused in the Bottleneck part of the C2f module to strengthen the model's ability to locate the target; In order to further improve the detection ability of small targets in patrol inspection, a small target detection layer is added to make the network pay more attention to the detection of small targets. Finally, in order to be deployed to the edge devices, the original convolution in the network is modified to a lightweight convolution GhostConv to reduce the number of model parameters. The experimental results show that the proposed method can accurately detect abnormal defects of transmission line components on the basis of improving the quality of inspection images. mAP is improved by 3.7% and the number of model parameters and calculation amount are greatly reduced, which reflects the effectiveness of the algorithm, and it has stronger extraction ability and robustness for subtle defect targets, meeting the detection requirements of power inspection.