RESRTDETR: Cross-Scale Feature Enhancement Based on Reparameterized Convolution and Channel Modulation

This paper proposes an innovative approach for detecting defects in insulators of overhead transmission lines. It employs a real-time object detector (RT-DETR) model named RESRTDETR to address the challenges faced by traditional methods in multi-object scenarios and complex backgrounds. The study addressed the issues of low accuracy and missed detections in traditional target detection algorithms for insulator defect detection. First, we propose the ResRepNet feature extraction network, which integrates the RepVGG reparameterized structure into existing backbone feature extraction networks to enhance the ability to extract features from defects of varying sizes. Secondly, since the information from neighboring channels surrounding insulator features plays a crucial role in small object detection, the ECA cross-channel attention mechanism is introduced. This leads to the proposal of the CA-CCFM module based on the CCFM module, enhancing the expressive capability of the head layer’s output features. Furthermore, to address the inherent limitations of the GIOU loss function in spatial localization, we introduce a SIoU loss function incorporating an angle penalty term to enhance the geometric awareness of bounding box regression. Finally, to enable the model to effectively learn features, a standard dataset comprising seven categories of samples was reconstructed to address the issues of imbalanced distribution and missing annotations in the CID insulator defect dataset. Data augmentation methods based on geometric transformations, color transformations, and mosaic stitching were employed to provide data support for subsequent experiments. Experiments validated the model’s effectiveness. Compared to existing object detection methods, RESRTDETR improves mAP@50 to 96.4% on the CID dataset and to 84.2% on the IDDD dataset. This approach not only enhances the accuracy of insulator defect detection but also plays a crucial role in maintaining the safety of power transmission systems, carrying far-reaching implications.