Research and Analysis of Algorithm for Detecting Surface Defects on Automotive Wheel Hubs Based on CCL-YOLOv8

Authors

• Yanjun Chen, Key Laboratory of Metallurgical Equipment and Control Units, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan 430081, China
• Min Zhou, Key Laboratory of Metallurgical Equipment and Control Units, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan 430081, China
• Meng Zha, Key Laboratory of Metallurgical Equipment and Control Units, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan 430081, China
• Meizhou Zhang, Key Laboratory of Metallurgical Equipment and Control Units, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan 430081, China

Abstract

Abstract: To address the challenges such as low detection efficiency, difficulties in identifying small defects, and poor accuracy in detecting surface defects on automotive wheel hubs, a lightweight neural network called CCL-YOLOv8 was proposed based on an improved YOLOv8n architecture. A synergistic improvement in both detection accuracy and efficiency was achieved through a three-stage model optimization strategy. A convolutional attention fusion module was introduced, which integrated convolution operations with self-attention mechanisms, thereby enhancing the model's ability to capture local features of small defects while perceiving global context under low signal-to-noise ratio conditions.A C2f-Star module was constructed to reduce computational overhead and enhance feature expression by optimizing feature interaction through element-wise multiplication. A lightweight shared detail-enhanced convolution detection head was designed to reduce the number of parameters and computational requirements and ensure high-precision detection by combining shared convolution and differential convolution. Experimental results demonstrate that the CCL-YOLOv8 algorithm significant enhancements are achieved in detection accuracy, model lightweighting, and computational efficiency compared to existing algorithms. Consequently, this approach effectively addresses the technical challenges in detecting surface defects on automotive wheel hubs and provides an efficient, lightweight, and precise solution for industrial real-time detection.

Recommended Citation

Chen, Yanjun; Zhou, Min; Zha, Meng; and Zhang, Meizhou (2026) "Research and Analysis of Algorithm for Detecting Surface Defects on Automotive Wheel Hubs Based on CCL-YOLOv8," Journal of System Simulation: Vol. 38: Iss. 3, Article 9.
DOI: 10.16182/j.issn1004731x.joss.25-0139
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss3/9

First Page

670

Last Page

686

CLC

TP391.9

Recommended Citation

Chen Yanjun, Zhou Min, Zha Meng, et al. Research and Analysis of Algorithm for Detecting Surface Defects on Automotive Wheel Hubs Based on CCL-YOLOv8[J]. Journal of System Simulation, 2026, 38(3): 670-686.

Corresponding Author

Zhou Min

DOI

10.16182/j.issn1004731x.joss.25-0139