Insulator String Detection Method Based on the InST-Net Network

Aiming at the problem of detecting insulator strings in aerial images, a detection method of insulator strings based on the InST-Net network is proposed in this paper. First, the ResNet50 network pretrained on the ImageNet dataset is used as the backbone network for insulator string feature extraction. Subsequently, for insulator strings of different imaging sizes in the image, three detection branches are designed based on the design ideas of the existing YOLO model. Finally, an SPP module is adopted to improve the feature extraction capability of each detection branch of the proposed InST-Net network. The experimental results show that the InST-Net network detection accuracy rate reaches 90.63%, which is higher than that of the four classic one-stage target detection networks and the existing insulator string detection network.

Automated summary

This paper proposes a detection method for insulator strings based on the InST-Net network, which uses a pretrained ResNet50 network for feature extraction and designs multiple detection branches and an SPP module to improve detection accuracy.