A real-time surface defect detection framework for synthetic-fiber bobbins

Surface defect detection for synthetic fiber bobbins is crucial to intelligent textile manufacturing. Existing vision-based methods are limited by the lack of task-specific datasets, the inefficient adaptation of detectors to this application, and the difficulty of deployment on edge devices. To address these challenges, this study proposes a lightweight detection framework. First, an image acquisition system equipped with a bobbin pose-adjustment device is developed, and a new industry-oriented dataset, FiberBobbin-40K, containing 40,497 high-quality images, is established to fill the gap in fiber bobbin defect detection. Subsequently, a computationally efficient detector adaptation strategy is proposed. Finally, a compression framework integrating a redesigned detection head, layer pruning, and channel pruning is developed to enable efficient inference on edge devices. Experimental results show that, compared with the conventional YOLOv8, the optimized model reduces the number of parameters by approximately 71%, while incurring only 2.03% and 2.49% decreases in mAP and F1 score, respectively. In addition, it achieves 94.73% accuracy and 26.37 FPS on a CPU. The optimized model has only 2.47 million parameters and a model size of 4.86 MB, which is substantially smaller than Faster R-CNN and YOLOv7 while maintaining competitive detection performance.