Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model
Abstract This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. The key innovations of SLF-YOLO include a novel SC_C2f module with a channel...
Saved in:
| Main Authors: | Yuan Liu, Yilong Liu, Xiaoyan Guo, Xi Ling, Qingyi Geng |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-94936-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD
by: Chao Chen, et al.
Published: (2025-01-01) -
YOLO-SUMAS: Improved Printed Circuit Board Defect Detection and Identification Research Based on YOLOv8
by: Ying Tang, et al.
Published: (2025-04-01) -
A Multi-Category Defect Detection Model for Rail Fastener Based on Optimized YOLOv8n
by: Mei Chen, et al.
Published: (2025-06-01) -
A lightweight algorithm for steel surface defect detection using improved YOLOv8
by: Shuangbao Ma, et al.
Published: (2025-03-01) -
A Lightweight Detection Method for Meretrix Based on an Improved YOLOv8 Algorithm
by: Zhongxu Tian, et al.
Published: (2025-06-01)