Infrared Image Detection of Substation Insulators Using an Improved Fusion Single Shot Multibox Detector

The thermographic diagnostic of substation insulators can facility to detect their insulation status in time. However, the detection effect of infrared insulator images is usually poor due to the complex background of substation scene images. In order to solve this problem and improve the automatic detection capability of substation equipment, an infrared insulator image detection model based on the improved feature fusion single shot multibox detector is proposed in this paper. The model combines multi-scale feature maps to generate a new feature pyramid, and designs a new feature enhancement module in the shallow network of the model to improve its ability to extract features of infrared images of substation insulators. Meanwhile, the clustering algorithm is used to calculate the target aspect ratio information of the dataset to realize the adaptive change of the aspect ratio of the default box. Furthermore, the transfer learning is employed to further improve the learning efficiency of the model. Before the formal experiment, the design scheme of feature enhancement module is confirmed by ablation studies. Experiments are performed on the collected infrared insulator images, instrument transformer images and Pascal VOC 2007 dataset. It is demonstrated experimentally that this model achieves excellent detection results.

Automated summary

This paper proposed an infrared insulator image detection model based on the improved feature fusion single shot multibox detector, which combines multi-scale feature maps to generate a new feature pyramid and designs a new feature enhancement module. By using clustering algorithm and transfer learning, the model achieved excellent detection results on collected datasets.