A Lightweight Aerial Power Line Segmentation Algorithm Based on Attention Mechanism

Power line segmentation is very important to ensure the safe and stable operation of unmanned aerial vehicles in intelligent power line inspection. Although the power line segmentation algorithm based on deep learning has made some progress, it is still quite difficult to achieve accurate power line segmentation due to the complex and changeable background of aerial power line images and the small power line targets, and the existing segmentation models is too large and not suitable for edge deployment. This paper proposes a lightweight power line segmentation algorithm—G-UNets. The algorithm uses the improved U-Net of Lei Yang et al. (2022) as the basic network (Y-UNet). The encoder part combines traditional convolution with Ghost bottleneck to extract features and adopts a multi-scale input fusion strategy to reduce information loss. While ensuring the segmentation accuracy, the amount of Y-UNet parameters is significantly reduced; Shuffle Attention (SA) with fewer parameters is introduced in the decoding stage to improve the model segmentation accuracy; at the same time, in order to further alleviate the impact of the imbalanced distribution of positive and negative samples on the segmentation accuracy, a weighted hybrid loss function fused with Focal loss and Dice loss is constructed. The experimental results show that the number of parameters of the G-UNets algorithm is only about 26.55% of that of Y-UNet, and the F1-Score and IoU values both surpass those of Y-UNet, reaching 89.24% and 82.98%, respectively. G-UNets can greatly reduce the number of network parameters while ensuring the accuracy of the model, providing an effective way for the power line segmentation algorithm to be applied to resource-constrained edge devices such as drones.