Steel surface defect detection and segmentation using deep neural networks
Defect detection is a crucial task in the manufacturing industry, particularly in steel surface inspection. While manual recognition is one of the most reliable techniques, recent advances in computer vision and machine learning have led to the development of automatic defect detection techniques. T...
Saved in:
Main Authors: | Sara Ashrafi, Sobhan Teymouri, Sepideh Etaati, Javad Khoramdel, Yasamin Borhani, Esmaeil Najafi |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-03-01
|
Series: | Results in Engineering |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302500060X |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
LDDP-Net: A Lightweight Neural Network with Dual Decoding Paths for Defect Segmentation of LED Chips
by: Jie Zhang, et al.
Published: (2025-01-01) -
Leveraging Segment Anything Model (SAM) for Weld Defect Detection in Industrial Ultrasonic B-Scan Images
by: Amir-M. Naddaf-Sh, et al.
Published: (2025-01-01) -
An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
by: Jinwen Wang, et al.
Published: (2024-12-01) -
Deep learning-enhanced defects detection for printed circuit boards
by: Van-Truong Nguyen, et al.
Published: (2025-03-01) -
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
by: Peng Zheng, et al.
Published: (2024-12-01)