| Issue |
E3S Web Conf.
Volume 233, 2021
2020 2nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)
|
|
|---|---|---|
| Article Number | 01032 | |
| Number of page(s) | 4 | |
| Section | NESEE2020-New Energy Science and Environmental Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202123301032 | |
| Published online | 27 January 2021 | |
A Semantic Segmentation Method for Buffer Layer Defect Detection in High Voltage Cable
State Grid Hunan Electric Power Company Limited Research Institute, Changsha 410007, China
a Corresponding author: sghnzj1988@163.com
A semantic segmentation method based on the fully convolutional network is proposed to detect the buffer layer defect in high voltage cable automatically. One hundred seventy-seven high-resolution X-ray images of cables are collected. FCN-8s and VGG16 backbone are adopted. The results indicated that the FCN-8s achieves the mIoU to 0.67 on the test set, proving to be an efficient way to detect the buffer layer defects.
© The Authors, published by EDP Sciences 2021
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