  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.
