A Lightweight Conditional Diffusion Segmentation Network Based on Deformable Convolution for Surface Defect Detection
Surface defect detection is crucial to industrial manufacturing and research for surface defects has drawn much attention. However, defects in industrial environment are very diverse. Because defects scale and poses are constantly changing and current methods lack the ability to model the deformatio...
Saved in:
| Main Authors: | Jiusheng Chen, Yibo Zhao, Haibing Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/jece/2935790 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things
by: Meixia Fu, et al.
Published: (2023-05-01) -
A lightweight steel surface defect detection network based on YOLOv9
by: Tianyi Zheng, et al.
Published: (2025-05-01) -
Lightweight Pyramid Cross-Attention Network for No-Service Rail Surface Defect Detection
by: Sixu Guo, et al.
Published: (2025-01-01) -
Research on Online Defect Detection Method of Solar Cell Component Based on Lightweight Convolutional Neural Network
by: Huaiguang Liu, et al.
Published: (2021-01-01) -
A high-precision segmentation network for industrial surface defect detection
by: Hao Chen, et al.
Published: (2025-05-01)