An Efficient and Lightweight Surface Defect Detection Method for Micro-Motor Commutators in Complex Industrial Scenarios Based on the CLS-YOLO Network
Qipeng Chen, Qiaoqiao Xiong, Haisong Huang, Saihong Tang
Abstract
Existing surface defect detection methods for micro-motor commutators suffer from low detection accuracy, poor real-time performance, and high false detection and missed detection rates for small targets. To address these issues, this paper proposes a high-performance and robust commutator surface defect detection model (CLS-YOLO), using YOLOv11-n as the baseline model. First, a lightweight Cross-Scale Feature Fusion Module (CCFM) is introduced to integrate features from different scales, enhancing the model’s adaptability to scale variations and ability to detect small objects. This approach reduces model parameters and improves detection speed without compromising detection accuracy. Second, a Large Separable Kernel Attention (LSKA) module is incorporated into the detection head to strengthen feature understanding and capture, reducing interference from complex surface patterns on the commutator and significantly improving adaptability to various target types. Finally, to address issues related to the center point location, aspect ratio, angle, and sample imbalance in bounding boxes, SIoU Loss replaces the CIoU Loss in the original network, overcoming limitations of the original loss function and enhancing overall detection performance. Model performance was evaluated and compared on a commutator surface defect detection dataset, with additional experiments designed to verify the model’s effectiveness and feasibility. Experimental results show that, compared to YOLOv11-n, the CLS-YOLO model achieves a 2.08% improvement in [email protected]. This demonstrates that CLS-YOLO can accurately detect large defect targets while maintaining accuracy for tiny defects. Additionally, CLS-YOLO outperforms most YOLO-series models, including YOLOv8-n and YOLOv10-n. The model’s parameter count is only 1.860 million, lower than YOLOv11-n, with a detection speed increase of 8.34%, making it suitable for deployment on resource-limited terminal devices in complex industrial scenarios.