A Two-Stage Corrosion Defect Detection Method for Substation Equipment Based on Object Detection and Semantic Segmentation

Corrosion defects will increase the risk of power equipment failure, which will directly affect the stable operation of power systems. Although existing methods can detect the corrosion of equipment, these methods are often poor in real-time. This study presents a two-stage detection approach that combines YOLOv8 and DDRNet to achieve real-time and precise corrosion area localization. In the first stage, the YOLOv8 network is used to identify and locate substation equipment, and the detected ROI areas are passed to the DDRNet network in the second stage for semantic segmentation. To enhance the performance of both YOLOv8 and DDRNet, a multi-head attention block is integrated into their algorithms. Additionally, to address the challenge posed by the scarcity of corrosion defect samples, this study augmented the dataset using the cut-copy-paste method. Experimental results indicate that the improved YOLOv8 and DDRNet, incorporating the multi-head attention block, boost the mAP and mIoU by 5.8 and 9.7, respectively, when compared to the original method on our self-built dataset. These findings also validate the effectiveness of our data augmentation technique in enhancing the model's detection accuracy for corrosion categories. Ultimately, the effectiveness of the proposed two-stage detection method in the real-time detection of substation equipment corrosion defects is verified, and it is 48.7% faster than the one-stage method.