Steel Surface Defect Detection Based on Improved GCHS-YOLO Algorithm
In this paper, we address challenges in steel surface defect inspection, such as missed detections and false detections, by proposing the GCHS-YOLO detection algorithm. Built on YOLOv8s, our approach replaces the traditional Feature Pyramid Network (FPN) in the NECK section with a multi-scale fusion...
Saved in:
Main Authors: | Ruiqiang Guo, Peiyong Ji, Yapin Zhang, Jingqi Hu, Wenlong Liu, Xuejian Li, Min Li |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10798110/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11
by: Yunlong Gao, et al.
Published: (2024-12-01) -
Impact of addition of ca on clogging of SEN and magnetic properties of non-oriented silicon steel
by: Kong W., et al.
Published: (2019-01-01) -
Steel surface defect detection and segmentation using deep neural networks
by: Sara Ashrafi, et al.
Published: (2025-03-01) -
An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
by: Jinwen Wang, et al.
Published: (2024-12-01) -
Quality Assessment Method for Chromite Sand to Reduce the Number of Cast Steel Surface Defects
by: T. Wróbel, et al.
Published: (2024-12-01)