Occluded Insulator Detection System Based on YOLOX of Multi-Scale Feature Fusion

As a special insulation control, insulators protect transmission lines. In the process of detecting insulators by UAV, locating the insulators is a prerequisite for defect detection. However, In complex transmission line contexts, heavily occluded insulators are abundant. This kind of insulator is difficult to locate accurately due to its small size and extreme aspect ratio. To address this issue, an occlusion insulator detection system based on YOLOX is proposed in this work. Firstly, an improved SPP module is used to extract the semantic information of the backbone's P5 layer. Secondly, the multi-feature fusion module of attention mechanism (AFF-BiFPN) fully uses deep, shallow, and original feature information, extracting effective information about occluded insulators and small defects. To enhance the accuracy of insulator localization and the detection performance of the network, an adaptive anchor frame coarse extraction method is proposed. In addition, a data augmentation is proposed to simulate the occluded insulators. Experiments show that the method can detect defective insulators in the foreground while locating the occluded insulators in the background. On the test set, the insulator identification precision is 90.71%, and the recall is 88.25%.