Automatic Segmentation Method of Center Screw of Power Grid Drainage Clamp Based on DeepLabv3+

The location of wire clamp hole based on computer vision is one of the key points to realize the automation of power grid drainage operation. The center screw of the wire clamp is an important mark of the position of the wire clamp hole, so the automatic segmentation of which is an effective means to realize the position of the wire clamp hole. Therefore, an automatic segmentation method for wire clamp center screw based on DeepLabv3+ is proposed in this work. The performance of the proposed method is also compared with that of the mainstream convolutional neural networks PspNet, SegNet and U-Net. The result shows that the segmentation of DeepLabv3+ not only achieves 96.78% overall accuracy, but also performs better in sensitivity and DICE similarity quantitative analysis. The proposed method could detect the automatic position of wire clamp hole, providing a proven and effective method for the automatic drainage operation of power grid.