收藏切换
Surface Defect Detection on Lightweight YOLOv8 Steel Incorporating MobileNetv3
收藏切换
PDF
Ming-qi HU1, Hui-ming CHEN1, Wei XU2, Cheng-jun GUO1, Qiu-ming LIU2, *
Science Technology and Engineering | 2025, 25(16) : 6831 - 6840
Less
收藏切换
Science Technology and Engineering | 2025, 25(16): 6831-6840
Papers·Automation and Computational Technology
Surface Defect Detection on Lightweight YOLOv8 Steel Incorporating MobileNetv3
Full
Ming-qi HU1, Hui-ming CHEN1, Wei XU2, Cheng-jun GUO1, Qiu-ming LIU2, *
Affiliations
  • 1 Advanced Copper Industry College, Jiangxi University of Science and Technology, Yingtan 335000, China
  • 2 School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China
Published: 2025-06-08 doi: 10.12404/j.issn.1671-1815.2404413
Outline
收藏切换

To address the high cost and low accuracy of manual inspection for steel surface defects, as well as the excessive computational resource requirements caused by complex traditional target detection models,a lightweight defect detection algorithm named YOLOv8n-MDC was proposed by integrating MobileNetv3 with YOLOv8.Firstly, based on YOLOv8n, the original intersection over union(IoU)-based bounding box loss function was replaced with weighted IoU(WIoU), enhancing model robustness through a non-monotonic focusing mechanism. Secondly, the backbone feature extraction network of YOLOv8n was substituted with MobileNetv3, utilizing its lightweight architecture to reduce network complexity and redundant computational overhead. Finally, during the feature fusion stage, depthwise separable convolution (DWConv) and C3Ghost modules replaced the original components, further minimizing model parameters and accelerating detection speed. Evaluated on the NEU-DET steel surface defect dataset, the YOLOv8n-MDC achieves an mAP of 81.3%, representing a 5% improvement over the baseline YOLOv8n, while its parameter count and computational complexity are reduced to 1.02 M and 2.1 GFLOPs (33.9% and 25.9% of the original model, respectively), meeting industrial requirements. This lightweight algorithm significantly reduces computational complexity and resource consumption while enhancing detection accuracy, offering an optimized solution for industrial steel surface defect inspection.

steel surface defects  /  defect detection  /  lightweight networking  /  YOLOv8  /  MobileNetv3
Ming-qi HU, Hui-ming CHEN, Wei XU, Cheng-jun GUO, Qiu-ming LIU. Surface Defect Detection on Lightweight YOLOv8 Steel Incorporating MobileNetv3[J]. Science Technology and Engineering, 2025 , 25 (16) : 6831 -6840 . DOI: 10.12404/j.issn.1671-1815.2404413
Year 2025 volume 25 Issue 16
PDF
443
167
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2404413
  • Receive Date:2024-06-13
  • Online Date:2025-07-09
  • Published:2025-06-08
Article Data
Affiliations
History
  • Received:2024-06-13
  • Revised:2025-03-04
Funding
Affiliations
    1 Advanced Copper Industry College, Jiangxi University of Science and Technology, Yingtan 335000, China
    2 School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2404413
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT