Substation Instrument Defect Detection Based on Multi-Domain Collaborative Attention Fusion

The detection of defects in substation instruments, such as surge arrester counters, is hindered by subtle characteristic changes and severe class imbalance. To address these challenges, this study proposes an enhanced detection algorithm based on multi-domain collaborative attention fusion (MDCAF). This method integrates three key contributions: hybrid enhancement to alleviate boundary blurring in transition samples; the MDCAF module, which collaboratively captures features across channel, space, and axis domains; and a class-weight balancing strategy to optimize learning for rare defects. The experimental results show that the average precision (mAP) is 90.1%, which is 2.8 percentage points higher than the baseline, reducing both missed and false detections.