Localization and Pixel-Confidence Network for Surface Defect Segmentation
Surface defect segmentation based on deep learning has been widely applied in industrial inspection. However, two major challenges persist in specific application scenarios: first, the imbalanced area distribution between defects and the background leads to degraded segmentation performance; second,...
Saved in:
| Main Authors: | Yueyou Wang, Zixuan Xu, Li Mei, Ruiqing Guo, Jing Zhang, Tingbo Zhang, Hongqi Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/15/4548 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Steel surface defect detection and segmentation using deep neural networks
by: Sara Ashrafi, et al.
Published: (2025-03-01) -
A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines
by: Aohua Li, et al.
Published: (2025-05-01) -
DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation
by: Yange Sun, et al.
Published: (2025-05-01) -
Study on detecting methods for apple stem and defected surface with computer vision
by: ZHANG Wen-ying, et al.
Published: (2001-09-01) -
Siamese network with change awareness for surface defect segmentation in complex backgrounds
by: Biyuan Liu, et al.
Published: (2025-04-01)