EPFD: An Electric Power Fitting Dataset and Benchmark for Object Detection

Ke Yang, Bangchao Zhai, Xiaoyu Zhao, Qianming Wang, Yongjie Zhai

Abstract: Intelligent detection of electric power fittings is essential for monitoring conditions and ensuring the stability of transmission and distribution lines. However, the lack of real-world aligned datasets has hindered progress. We developed an electric power fitting dataset that includes challenges, such as long-tailed distribution, multimorphology, multiscale, small targets, and dense occlusion. It contains 1560 unmanned aerial vehicle (UAV)-captured images of 18 types of electric power fittings, annotated with over 13 500 bounding boxes. Compared to existing datasets, ours offers significant advantages in categories, diversity, and complexity. We further propose a multimorphology contrastive learning approach to ensure consistent feature representation and address morphological diversity. By minimizing intraclass variation and maximizing interclass separability, our method achieves more discriminative feature representations. Experimental results indicate that our method achieves an average precision of 86.2% for multifitting detection—improving upon the current state-of-the-art by 2.6%. Meanwhile, cross-dataset testing further verifies the broad applicability of our approach.

External IDs:dblp:journals/tii/YangZZWZ25