PTSRDet: End-to-End Super-Resolution and object-detection approach for small defect detection of power transmission lines

Detecting small defects in power transmission lines is crucial for ensuring the safety and reliability of power grids. Unmanned Aerial Vehicle (UAV) imagery can be utilized for this purpose; however, the small size of defects and the low resolution of images make this task challenging. In this paper, we introduce a novel model that combines super-resolution and object detection techniques to address this issue. Our approach employs a Power Transmission Lines Single Image Super-Resolution Generative Adversarial Network (PTSRGAN) for super-resolution, and a new architecture for object detection that includes the HorNet backbone, a Super-Resolution based Path Aggregation Feature Pyramid Network (SR-PAFPN) neck, and a You Only Look Once-X (YOLOX) decoupled head.

The SR-PAFPN neck enhances feature quality and diversity, particularly for small defects, by integrating feature super-resolution during training. To further improve the accuracy of small defect detection, our model is trained end-to-end, allowing the super-resolution model to receive feedback from the object detection model and adapt accordingly. Extensive experiments demonstrate the effectiveness and efficiency of our Power Transmission Lines Super Resolution Defect Detection (PTSRDet) method. Our model achieves a precision of 92.87% and a recall of 96.32%, processing each image in just 0.34 s. These results highlight the model’s capability to accurately detect small defects in power transmission lines, making it a valuable contribution to the field.