PL-UNeXt: Per-stage Edge Detail and Line Feature Guided Segmentation for Power Line Detection
This addresses power line detection for electricity companies and drone obstacle avoidance, representing an incremental improvement with specific gains.
The paper tackles the problem of power line detection in aerial images by introducing PL-UNeXt, a segmentation model that uses edge detail and line feature guidance, achieving a 70.6 F1 score (+1.9%) on TTPLA and 68.41 mIoU (+5.2%) on VITL while maintaining real-time performance.
Power line detection is a critical inspection task for electricity companies and is also useful in avoiding drone obstacles. Accurately separating power lines from the surrounding area in the aerial image is still challenging due to the intricate background and low pixel ratio. In order to properly capture the guidance of the spatial edge detail prior and line features, we offer PL-UNeXt, a power line segmentation model with a booster training strategy. We design edge detail heads computing the loss in edge space to guide the lower-level detail learning and line feature heads generating auxiliary segmentation masks to supervise higher-level line feature learning. Benefited from this design, our model can reach 70.6 F1 score (+1.9%) on TTPLA and 68.41 mIoU (+5.2%) on VITL (without utilizing IR images), while preserving a real-time performance due to few inference parameters.