PLD-DETR: A Method for Defect Inspection of Power Transmission Lines

Unmanned Aerial Vehicle (UAV)-based computer vision has emerged as a crucial approach for transmission line defect detection. However, transmission lines contain multi-scale components in complex environments, thereby complicating the accurate extraction of multi-scale features and necessitating a careful balance between model complexity with detection accuracy. This paper proposes a Transformer-based framework called Power Line Defect Detection Transformer (PLD-DETR). To simultaneously capture shallow texture and deep semantic information while avoiding single-path limitations, a dual-domain selection mechanism block is designed as the backbone network, enabling collaborative feature extraction at different levels. Subsequently, an adaptive sparse self-attention mechanism is introduced to dynamically adjust attention weights for improved processing of critical feature regions, aiming to enhance attention to semantically rich regions and reduce background interference. Finally, we construct a multi-branch auxiliary bidirectional feature pyramid network to address information loss in traditional feature fusion. It fuses multi-scale features from four backbone layers through top-down and bottom-up bidirectional information flow, significantly improving feature representation capability. While maintaining model lightness, experimental results demonstrate that PLD-DETR achieves 2.7%, 7.01%, and 5.58% improvements in AP50, AP75, and AP50–95, respectively, compared to the baseline model. Compared with other transmission line defect detection methods, PLD-DETR demonstrates superior performance in both accuracy and efficiency