Typical wire clamps segmentation of transmission lines based on infrared image

In view of the current image segmentation field, there are few studies on the segmentation of typical wire clamp components of transmission lines. Traditional image processing methods have low segmentation accuracy and require artificial design of feature extraction methods, which are usually only suitable for equipment of a certain structure with insufficient generalization. In this paper, an infrared image segmentation method based on Mask R-CNN (Mask region-based convolutional neural network) for typical guide-ground lines is proposed. Its structure takes Mask R-CNN model combined with FPN (Feature pyramid structure) as the basic framework, and uses RPN (Regional proposal network) to generate candidate regions. Features are extracted from each candidate region through RoI Align layer, and then connected to FC (Fully connected layer) to achieve target classification and bbox (bounding box) regression. A mask branch is also added to predict the segmentation mask. The design can integrate multi-scale and multi-level semantic information to improve the recognition rate when extracting image features. In addition, the network structure is optimized by single channel for infrared images to reduce the size of the model and make it more lightweight. Ablation experiments were performed on two GTX 2080Ti graphics cards to verify the effectiveness of the proposed structure, and the mAP (mean average accuracy) of 0.421 was achieved with an IoU (Intersection over Union) threshold of 0.5.