Small-target images of transmission lines are prone to motion blur owing to factors such as air disturbance,rotor vibration,and relative motion during unmanned-aerial-vehicle inspections.This blurring leads to the loss of texture details,rendering small-target detection difficult.To address this problem,this study proposes a method for detecting small targets in transmission lines based on motion-blurred-image restoration.The proposed method utilizes a conditional vision Transformer-based generative adversarial network(ViT-GAN)to restore motion-blurred images of small targets,thereby enhancing the involved feature-extraction backbone's ability to perceive the global and regional information in images and improving the quality of image restoration for subsequent object detection.The involved YOLOv8 network is enhanced by introducing a multi-head self-attention mechanism,adding a small-target detection layer,and optimizing the boundary-frame-loss function.This helps to achieve good small-target detection in transmission line environments with complex backgrounds and large target-scale variations.Experimental results demonstrate that the proposed method can be used for accurate small-target detection for transmission lines.The average recognition accuracy of six categories of small targets is 92.77%,with an average recall rate of 94.19%,and average F1-score of 94.94%.Overall,the proposed method effectively mitigates the problem of missing and false detection,demonstrating its high accuracy and robustness.
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