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.
电源线路检测是电力公司的一项关键检查任务,同时也可用于避免无人机障碍。由于复杂的背景和像素比例较低,在平面图像中准确分离电源线路与周围区域仍然是一项挑战。为了正确捕捉空间边缘细节先验和线特征的指导,我们提供了PL-UNeXt,它是一个电源线路分割模型,采用增强训练策略。我们设计的边缘细节头计算边缘空间的损失,以指导较低级别细节学习,以及线特征头生成auxiliary segmentation masks,以监督更高级别的线特征学习。受益于此设计,我们的模型可以在TTPLA上获得70.6 F1得分(+1.9%),在VITL上获得68.41 mIoU(不使用红外图像) (+5.2%),同时由于 few inference parameters,仍保留实时性能。