An Efficient Multi-Scale Feature Fusion Network for Tiny Defect Detection on Ceramic Cup Surfaces

In ceramic cup manufacturing, manual inspection is prone to missed detections and false positives, particularly for small surface defects. To address these challenges, this study presents an effective and efficient YOLOv11m-based detection framework, termed CEL-YOLOv11m, for precise identification of small-scale defects on ceramic surfaces. Specifically, a multi-scale convolution module (EMSC) is introduced to enhance the backbone feature extraction structure. By integrating convolution kernels of varying sizes, the module improves multi-scale feature representation, while grouped convolution is employed to reduce computational overhead. In the feature aggregation stage, a CRGseg-based structure is incorporated, and a refinement component (RCM) is designed to strengthen fine-grained information for small targets. Additionally, a cross-scale feature fusion strategy is applied to improve contextual representation across different resolutions. For the detection stage, a Layer-shared Detail-Enhanced Convolutional Detection Head (LSDECD) is adopted to improve fine-grained localization while improving computational efficiency through parameter sharing. Experiments conducted on a self-constructed ceramic defect dataset and the VisDrone2019 benchmark show that the proposed framework achieves competitive performance compared with representative methods. The model attains an mAP@50(%) of 54.8% with an inference speed of 89.9 FPS, providing a favorable trade-off between detection accuracy and computational efficiency while maintaining strong precision in small defect detection.