Attention-Enhanced Lightweight YOLO with Evolutionary Architecture Search for Insulator Defect Detection

With the development of Internet of Things- and unmanned aerial vehicle (UAV)-based power inspection, the accurate and efficient detection of insulator defects has become the key to the safe and stable operation of transmission lines. However, in real UAV inspection scenarios, insulator defect detection is still faces many challenges, such as complex backgrounds, large-scale variations, small defect regions, and weak fault-related features. Existing lightweight detection models are often difficult to achieve a good balance between detection accuracy and computational efficiency. To address these problems, we propose an insulator defect detection method, YOLOv12n-HEPPSA, which combines the Pooling Partial Self-Attention (PPSA) mechanism with evolutionary neural architecture search (ENAS). A PPSA module is introduced at the end of the backbone network of YOLOv12n to enhance its ability to represent high-level features, so as to capture the differences between insulator defect regions and their adjacent normal structures. On this basis, an evolutionary search is performed by optimizing detection accuracy and computational cost. In addition, a historical-memory-guided evolutionary search and evaluation strategy is designed to reduce the randomness of the search process and improve optimization efficiency of candidate architectures. The experimental results obtained using the publicly available Unified Insulator Public Dataset (UPID) and four external datasets demonstrate that the proposed method balances detection accuracy and computational efficiency while exhibiting strong lightweight features and transferability across diverse UAV-based power inspection scenarios.