Vision-Based Power Line Segmentation With an Attention Fusion Network

Automatic power transmission line detection plays the key role in smart grid which has been widely applied into the path planning and navigation of intelligent inspection platforms, such as Unmanned Aerial Vehicles (UAVs), climbing robots, hybrid inspection robots, etc. Nevertheless, the power lines are always against complex background environment and different illumination conditions. And the power lines occupy a minimal portion image pixels in the aerial images compared with backgrounds which causes the foreground-background class imbalance issue. Therefore, robust and accurate vision-based power line detection still faces a certain challenge. Recently, deep learning has got fast development on pixel-level object segmentation due to strong contextual feature expression ability, especially U-shape network (U-Net) and its variants. However, it still exists a certain shortcomings owing to insufficient process of local contextual features to affect the segmentation precision. Meanwhile, multiple pooling operations in deep convolutional neural networks (DCNNs) also will cause the information loss. To address these issues, with the encoder-decoder architecture, a novel vision-based power line detection network is proposed in this paper to construct an end-to-end detection scheme of power lines from aerial images. To make the segmentation network capture the global contexts and emphasize target regions of power transmission lines, an attention block is proposed to be embedded into the proposed power line detection network to address the class imbalance issue. Meanwhile, faced with the insufficient process of local contextual feature maps of DCNNs, an attention fusion block is proposed for multi-scale feature fusion to acquire more rich information and improve the segmentation precision. Experiments on power lines show that the proposed power line detection network shows a good segmentation performance on real power line environment compared with other advanced detection methods.