红外技术2025，Vol.47Issue(6)：770-778,9.
改进U-net的电气设备紫外图像放电光斑分割
UV Image Discharge Spot Segmentation for Electrical Equipment Based on Improved U-net
申万科 1李罗璟懿 1方春华 1江全才 1陆杰炜 2夏星宇 1彭万钊1
作者信息
- 1. 三峡大学电气与新能源学院,湖北宜昌 443002
- 2. 国网浙江省电力有限公司开化县供电公司,浙江衢州 324000
- 折叠
摘要
Abstract
This paper proposes a semantic segmentation model called VA-Unet,designed to address the challenges of complex backgrounds,slight spot separation,complex feature selection,and low segmentation accuracy encountered in ultraviolet(UV)detection tasks of electrical equipment.VA-Unet incorporates the VGG16 feature extraction module and transfer learning to accelerate training and enhance the model's generalization capability.Additionally,an Attention Gate is integrated to improve segmentation precision by focusing on relevant features,enabling accurate detection of UV discharge spots in images.To address the issue of sample imbalance in the UV discharge spot dataset,VA-Unet employs a hybrid loss function in place of a conventional single loss function.Experimental results demonstrate that VA-Unet achieves superior performance in the precise localization and accurate segmentation of UV discharge spots.The model attains an IoU of 84.09％,PA of 88.20％,and F1-score of 91.35％,representing improvements of 14.41％,3.24％,and 9.22％,respectively,compared to the baseline U-Net model.关键词
紫外检测/语义分割/U-net/迁移学习/注意力机制Key words
ultraviolet detection/semantic segmentation/U-net/migration learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
申万科,李罗璟懿,方春华,江全才,陆杰炜,夏星宇,彭万钊..改进U-net的电气设备紫外图像放电光斑分割[J].红外技术,2025,47(6):770-778,9.
