Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.

基于摄像机的传输线检测（TLD）是飞机自动巡逻电力线的一项基本且至关重要的任务。在实例分割的推动下，本文提出了一种TLD算法，该算法采用了一种新型的深度神经网络，即CableNet。考虑到传输线的特定外观特性，基于全卷积网络（FCN）设计了网络结构，并进行了两项重大改进。首先，重叠的膨胀卷积层和空间卷积层被配置为更好地表示连续的长而细的电缆形状。其次，安排两个输出分支以生成多维特征图以进行实例分割。因此，可以同时检测电缆像素并为其分配电缆ID。在航拍图像上进行了多次实验，结果表明，该算法具有可靠的检测性能，优于传统的TLD方法。同时，可以将分割后的像素准确地识别为电缆实例，从而为进一步的应用进行线路拟合做出了贡献。