Song Jiang, Qian Jianguo, Li Yongrong, Liu Zhengjun, Chen Yiming, Chen Jianchang
Chinese Academy of Surveying & Mapping, Beijing 100036, China.
School of Geomatics, Liaoning Technical University, Fuxin 123000, China.
Sensors (Basel). 2022 Aug 26;22(17):6431. doi: 10.3390/s22176431.
Automatic power line extraction from aerial images of unmanned aerial vehicles is one of the key technologies of power line inspection. However, the faint power line targets and complex image backgrounds make the extraction of power lines a greater challenge. In this paper, a new power line extraction method is proposed, which has two innovative points. Innovation point one, based on the introduction of the Mask RCNN network algorithm, proposes a block extraction strategy to realize the preliminary extraction of power lines with the idea of "part first and then the whole". This strategy globally reduces the anchor frame size, increases the proportion of power lines in the feature map, and reduces the accuracy degradation caused by the original negative anchor frames being misclassified as positive anchor frames. Innovation point two, the proposed connected domain group fitting algorithm solves the problem of broken and mis-extracted power lines even after the initial extraction and solves the problem of incomplete extraction of power lines by background texture interference. Through experiments on 60 images covering different complex image backgrounds, the performance of the proposed method far exceeds that of commonly used methods such as LSD, Yolact++, and Mask RCNN. DSC, TPR, precision, and accuracy are as high as 73.95, 81.75, 69.28, and 99.15, respectively, while FDR is only 30.72. The experimental results show that the proposed algorithm has good performance and can accomplish the task of power line extraction under complex image backgrounds. The algorithm in this paper solves the main problems of power line extraction and proves the feasibility of the algorithm in other scenarios. In the future, the dataset will be expanded to improve the performance of the algorithm in different scenarios.
从无人机航拍图像中自动提取电力线是电力线巡检的关键技术之一。然而，电力线目标微弱且图像背景复杂，使得电力线的提取面临更大挑战。本文提出了一种新的电力线提取方法，该方法有两个创新点。创新点一，在引入Mask RCNN网络算法的基础上，提出了一种分块提取策略，以“先局部后整体”的思路实现电力线的初步提取。该策略全局减小了锚框尺寸，增加了特征图中电力线的比例，并减少了原始负锚框被误分类为正锚框所导致的精度下降。创新点二，所提出的连通域分组拟合算法解决了即使经过初始提取后电力线仍存在断裂和误提取的问题，以及背景纹理干扰导致电力线提取不完整的问题。通过对60幅涵盖不同复杂图像背景的图像进行实验，所提方法的性能远远超过了常用方法，如LSD、Yolact++和Mask RCNN。DSC、TPR、精度和准确率分别高达73.95、81.75、69.28和99.15，而FDR仅为30.72。实验结果表明，所提算法具有良好的性能，能够在复杂图像背景下完成电力线提取任务。本文算法解决了电力线提取的主要问题，并证明了该算法在其他场景下的可行性。未来，将扩大数据集以提高算法在不同场景下的性能。
