论文标题
PLGAN：用于航空图像中电力线分割的生成对抗网络
PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images
论文作者
论文摘要
各种航空图像中电力线的准确分割对于无人机飞行安全非常重要。但是，复杂的背景和非常薄的电源线结构使其在计算机视觉中本质上是一项固有的任务。本文介绍了PLGAN，这是一种基于生成对抗网络的简单而有效的方法，可从具有不同背景的航空图像分割电源线。我们通过考虑更多的上下文，几何形状和电源线的外观信息，而不是直接使用对抗网络来生成分割，而是采用了它们的某些解码功能并将其嵌入另一个语义分割网络。我们进一步利用生成的图像的适当形式来嵌入高质量的特征，并在Hough-Transform参数空间中定义新的损耗函数，以增强非常薄的功率线的分割。广泛的实验和全面的分析表明，我们提出的PLGAN优于先前的语义分割和线检测方法。
Accurate segmentation of power lines in various aerial images is very important for UAV flight safety. The complex background and very thin structures of power lines, however, make it an inherently difficult task in computer vision. This paper presents PLGAN, a simple yet effective method based on generative adversarial networks, to segment power lines from aerial images with different backgrounds. Instead of directly using the adversarial networks to generate the segmentation, we take their certain decoding features and embed them into another semantic segmentation network by considering more context, geometry, and appearance information of power lines. We further exploit the appropriate form of the generated images for high-quality feature embedding and define a new loss function in the Hough-transform parameter space to enhance the segmentation of very thin power lines. Extensive experiments and comprehensive analysis demonstrate that our proposed PLGAN outperforms the prior state-of-the-art methods for semantic segmentation and line detection.
