Shape-aware and feature fused power line detection network

Unmanned Aerial Vehicles (UAVs) have demonstrated flexibility and efficiency in power system inspection. Automatic power line detection is crucial to prevent collisions of UAVs, typically using visible or infrared (IR) sensors. In practice, infrared sensors are typically employed under adverse weather conditions due to their robustness to atmospheric interference, while visible-light sensors are preferred in clear weather to leverage their high resolution and detailed spectral information. However, this task remains highly challenging due to complex backgrounds and the inherently thin, elongated structure of power lines, which occupy minimal space in aerial imagery. These factors make power line detection a significant and ongoing problem in computer vision. Recently, convolutional neural networks (CNNs) have shown strong performance in semantic segmentation tasks. Yet, they often struggle with severe class imbalance, such as in power line detection, where background pixels dominate. To address these challenges, we propose a novel Shape-aware and Feature Fused Power Line Detection Network (SFFPLDN). Our approach includes: Shape-Aware Block (SAB) that enhances feature propagation from power line regions, and Spatial and Channel Attention Fusion Block (SCAFB) that adaptively merges multi-scale features by jointly evaluating spatial and channel-wise importance. We evaluate SFFPLDN on a public dataset containing both Infrared-IR and Visible-Light image sets. Extensive experiments demonstrate the superiority of our method. For the Infrared-IR subset, SFFPLDN achieves a sensitivity (S_e) of 0.859, a dice coefficient of 0.882, and an Area Under Curve-Precision Recall (AUC_pr) of 0.921. For the Visible-Light subset, it attains a sensitivity of 0.863, a dice coefficient of 0.860, and an AUC_pr of 0.931. These results not only validate the effectiveness of each proposed module but also establish a new state-of-the-art benchmark for power line detection.