SaSnet:基于自监督学习的电力线实时分割网络

Small and Strong: Power Line Segmentation Network in Real Time Based on Self-supervised Learning

电力线分割对于实现无人机自动避障、保障无人机低空飞行安全具有重要意义。传统基于线和线段的算法只能在一些简单场景下应用,在复杂场景下极容易出现误检、漏检情况。近年来,深度学习的快速发展极大地促进了电力线分割研究,但是相关研究仍然存在3个问题:1)较少考虑实际应用;2)没有充分利用电力线特点;3)忽略了缺乏大规模电力线数据的问题。该文基于电力线分割的实际应用需求,改进了传统的F1分数评价指标,提出一种基于积分加权的电力线分割评价指标。同时从电力线特点出发,提出一个轻量级的实时语义分割网络SaSnet,包括lite和general两个深度不同的版本。针对电力线分割缺乏大规模数据的问题,提出自监督学习算法IBS,基于IBS算法的SaSnet用极少量有标签数据进行训练,在公开数据集上的精度和速度都超越了目前已有的最优方法。在嵌入式设备上的实验结果表明SaSnet已经初步具备了实际应用的能力。

Power line segmentation is of great significance to the automatic obstacle avoidance and the low-altitude flight safety of unmanned aerial vehicles. Whereas the traditional line-based and segment-based algorithms can only be applied in some simple scenes, they are prone to give false positive and false negative results in complex scenes. In recent years, the rapid development of deep learning has greatly promoted the research of power line segmentation. However, there are still three problems we observed in related research: 1) Less consideration of practical application;2) inadequate use of power line characteristics;3) ignoring the problem of lacking large-scale power line data. Starting from the application requirements of power line segmentation, this paper improved the traditional F1-Score evaluation matrix, and proposed a more suitable one, termed WG-F1-Score, for power line segmentation. Furthermore, in view of the characteristics of power line, this paper proposed a lightweight real-time semantic segmentation network, dubbed as SaSnet, which included two versions with different depths, lite and general. To solve the problem of lacking large-scale data for power line segmentation, this paper proposed a self-supervised learning algorithm, inpainting based self-supervised learning(IBS).Based on the IBS algorithm, trained with a very small amount of labeled data, SaSnet achieved the state-of-the-art performance in both segmentation performance and inference speed on the public dataset. The experiment results on the Jetson AGX Xavier show that SaSnet has preliminary capabilities for practical applications.