Volume 44 Issue 12
Dec.  2022
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Article Contents
LIU He, ZHAO Tiancheng, LIU Junbo, JIAO Lixin, XU Zhihao, YUAN Xiaocui. Deep Residual UNet Network-based Infrared Image Segmentation Method for Electrical Equipment[J]. Infrared Technology , 2022, 44(12): 1351-1357.
Citation: LIU He, ZHAO Tiancheng, LIU Junbo, JIAO Lixin, XU Zhihao, YUAN Xiaocui. Deep Residual UNet Network-based Infrared Image Segmentation Method for Electrical Equipment[J]. Infrared Technology , 2022, 44(12): 1351-1357.

Deep Residual UNet Network-based Infrared Image Segmentation Method for Electrical Equipment

  • Received Date: 2022-03-25
  • Rev Recd Date: 2022-04-29
  • Publish Date: 2022-12-20
  • Infrared thermal image processing is an effective method for detecting defects in electrical equipment. Aiming at the problem of electrical equipment segmentation in infrared thermal images with a complex background, in this study we propose a deep residual UNet network for infrared thermal image segmentation. Using a deep residual network to replace VGG16 to perform feature extraction and coding for the UNet network, a deep residual series UNET network was constructed to segment electrical equipment. To validate the effectiveness of the Res-UNet network, infrared images, including current transformers and circuit breakers, were used to test the segmentation results and were compared with the traditional UNet and Deeplabv3+ networks. The networks were tested using 876 images. The experimental results show that RES18-UNET can accurately segment electrical equipment; the segmentation precision of current transformers and circuit breakers is greater than 93%, and the mean intersection over union (MIoU) is greater than 89%. Our method obtains more accurate segmentation results than UNet and Deeplabv3+, setting the basis for intelligent diagnosis of electrical faults.
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