Abstract
Aiming at the problems of complex background, diverse shapes, and object occlusion in aerial images, a cascade reasoning graph network (CRGN) is proposed for multi-fitting detection on transmission lines. First of all, for these three problems mentioned above, co-occurrence knowledge, semantic knowledge, and spatial knowledge were constructed to represent the co-relation of objects by analyzing the characteristics of the transmission line fittings. Next, the Supervised Graph Learning (SGL), Graph Attention network (GAT), and Graph Convolutional Network (GCN) were employed to reason corresponding knowledge. In addition, to generate more accurate proposals for the graph reasoning module, resampling was carried out through the cascade network. Finally, the enhanced features were fused with the original visual features to recognize and position the fittings. Test results show that CRGN can improve the detection effect of multi-fittings on the transmission line, especially for some hard-detection fittings.
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