Application of Partial Differential Segmentation Model with Adaptive Weight in Infrared Image of Substation Equipment

To address the problem whereby the equipment area cannot be accurately segmented in the infrared image while maintaining substation equipment, this study applied an improved adaptive weight partial differential image segmentation method to segment the equipment area. By analyzing problems such as low signal-to-noise ratio, blurred edges, low contrast, and uneven grayscale of images, we investigated the disadvantages of traditional image segmentation methods, and the segmentation model based on partial differential equations was improved. The proposed adaptive weight LGIF model utilizes the different gray scale inhomogeneity of the target equipment and the background, associated it with the respective average gray scale, and adjusted the weights of the model's global and local energy items. Experiments in a variety of scenarios have verified that the model in this study is more effective and accurate than the OTSU method, CV model, and fixed-weight LGIF model, which is convenient for follow-up feature extraction and recognition.