Liu, X., Miao, X., Jiang, H., Chen, J., Wu, M., & Chen, Z. (2022). Component Detection for Power Line Inspection Using a Graph-based Relation Guiding Network. IEEE Transactions on Industrial Informatics, 1–11. https://doi.org/10.1109/tii.2022.3227638
Detecting the components in aerial images is a crucial task in automatic visual inspection for power lines. Currently, deep learning models guided by external knowledge have achieved promising performances compared to directly applying the benchmark detectors. However, the component relationship, as human commonsense knowledge for object reasoning, is rarely investigated in this field. This study presents a graph-based relation guided network for power line component detection, which exploits correlations of regions, images, and categories. The visual relation module is employed to learn region-to-region relationship and enhance the visual features of each proposal that may contain components. Meanwhile, two guidance modules are proposed to capture image-to-region correlation and distinctively facilitate the category classification and position regression, which has not been considered in previous methods. Moreover, the category graphs built in these two modules are able to explore category-to-category dependencies that can further promote the network ability. Experimental results demonstrate that the proposed method can achieve more accurate and reasonable component detection compared to previous methods, which verifies the effectiveness of the proposed model incorporated with relation knowledge.
This work was supported in part by the Science and Technology Project of State Grid Corporation of China under Grant 00102126, in part by the Industry-University Cooperation Project in Fujian Province
University under Grant 2022H6020, and in part by the program of China Scholarship Council under Grant 202106650019.