Automation Department, North China Electric Power University, Baoding 071003, China.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2022 Aug 15;22(16):6102. doi: 10.3390/s22166102.
To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial images with complex backgrounds and different scales. The spatial geometric reasoning sub-module (SGR) was developed to represent the spatial location relationship of defects. The appearance geometric reasoning sub-module (AGR) and the parallel feature transformation (PFT) sub-module were adopted to obtain the appearance geometric features from the real samples. These multi-geometric features can be fused with the original visual features to identify and locate the insulator defects. The proposed solution is assessed through experiments against the existing solutions and the numerical results indicate that it can significantly improve the detection accuracy of multiple insulator defects using the aerial images.
为了解决基于无人系统的智能输电线路绝缘子检测中的挑战，本文提出了一种多几何推理网络（MGRN），以基于具有复杂背景和不同尺度的航空图像准确检测绝缘子几何缺陷。空间几何推理子模块（SGR）用于表示缺陷的空间位置关系。采用外观几何推理子模块（AGR）和并行特征变换（PFT）子模块从真实样本中获取外观几何特征。这些多几何特征可以与原始视觉特征融合，以识别和定位绝缘子缺陷。通过与现有解决方案的实验评估，结果表明，该方法可以显著提高使用航空图像检测多种绝缘子缺陷的准确性。
