Defect Target Detection Network Integrating Depth Spatial Information and Adaptive Progressive Feature Fusion for Power Transmission and Distribution Lines

Transmission and distribution networks are evolving into distributed smart grids, making accurate defect localization of line components increasingly important for power inspection. However, traditional manual inspection is inefficient and unreliable in complex scenarios, while most existing deep learning methods rely only on two-dimensional visible-light images and struggle to capture spatial structure and occlusion relationships. To address these limitations, this paper proposes a defect detection network that integrates monocular relative-depth information with adaptive progressive feature fusion. The framework adopts a dual-branch architecture, where visible-light images provide appearance information and relative-depth maps generated from the corresponding RGB frames using Depth Anything V2 encode auxiliary spatial relationships. Because both modalities originate from the same image frame, no additional depth sensor or cross-sensor temporal synchronization is required. An adaptive progressive fusion module performs coarse-to-fine cross-modal interaction, and a coordinate attention mechanism is introduced to enhance positional encoding and suppress background interference. Experiments on a self-constructed dataset of 9838 RGB images paired with estimated relative-depth maps covering five typical defect categories show that the proposed method achieves 92.4% mAP@50 and 67.1% mAP@50–95. The results demonstrate its effectiveness in improving defect localization, small-object detection, and robustness across the evaluated transmission and distribution line scenes.