DeepLabv3+ Network-based Infrared Image Segmentation Method for Current Transformer
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摘要: 红外图像智能分析是变电设备故障诊断的一种有效方法，目标设备分割是其关键技术。本文针对复杂背景下电流互感器整体分割难的问题，采用基于ResNet50的DeepLabv3+神经网络，用电流互感器的红外图像训练语义分割模型的方法，对收集到的样本采用限制对比度自适应直方图均衡化方法实现图像轮廓增强，构建样本数据集，并运用图像变换扩充样本数据集，搭建语义分割网络训练语义分割模型，实现电流互感器像素与背景像素的二分类。通过文中方法对420张电流互感器红外图像测试，结果表明，该方法的平均交并比（Mean Intersection over Union, MIoU）为87.5%，能够从测试图像中精确分割出电流互感器设备，为后续电流互感器的故障智能诊断做铺垫。
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关键词:
- 红外图像 /
- 电流互感器 /
- ResNet50 /
- DeepLabv3+ /
- 语义分割
Abstract: Infrared image intelligent analysis is an effective method for the fault diagnosis of transformer equipment, and its key technology is target device segmentation. In this study, aiming to address the difficulty in overall segmentation of current transformers with complex backgrounds, the DeepLabv3+ neural network based on ResNet50 was applied to train the semantic segmentation model with infrared image of CT. The collected samples were enhanced by the limited contrast adaptive histogram equalization method, and a sample dataset was constructed. The sample dataset was expanded by image distortion, and a semantic segmentation network was built to train the semantic segmentation model to realize the binary classification of current transformer pixels and background pixels. The test results of 420 current transformer infrared images showed that the MIOU of this method is 87.5%, which can accurately divide the current transformer equipment from the test images and lay a foundation for the subsequent intelligent fault diagnosis of current transformers.-
Key words:
- infrared image /
- current transformer /
- ResNet50 /
- DeepLabv3+ /
- semantic segmentation
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图 6 基于Resnet50的DeepLabv3+网络模型训练过程
Figure 6. Training process of DeepLabv3+ network model based on ResNet50
表 1 多种模型测试数据表
Table 1. Test data table of various models
Model Categories Accuracy IoU MIoU DeepLabv3+（ResNet50） CT 0.86 0.77 0.855 Background 0.95 0.94 DeepLabv3+（ResNet18） CT 0.81 0.72 0.81 Background 0.92 0.90 SegNet CT 0.67 0.44 0.615 Background 0.86 0.79 FCN-8s CT 0.75 0.63 0.74 Background 0.89 0.85 
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表 2 基于ResNet50的DeepLabv3+模型加入后处理前后测试对比
Table 2. Comparison of tests before and after the addition of the DeepLabv3+ model based on ResNet50
Model Categories Accuracy IoU MIoU DeepLabv3+（ResNet50） CT 0.86 0.77 0.855 Background 0.95 0.94 Our algorithm CT 0.87 0.79 0.875 Background 0.97 0.96
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