SF-Yolov8n: A Novel Ultralightweight and High-Precision Model for Detecting Surface Defects of Dental Nails

To improve the detection accuracy of surface defects in dental nails in the current medical manufacturing industry, while reducing the model size and making it easy to deploy on resource-limited devices, we propose a novel model structure for detecting surface defects on dental nails, SF-Yolov8n, based on Yolov8n. Its characteristic is that the model is not only lightweight but also has high detection performance. First, to significantly reduce the number of parameters and volume of the model, while improving the detection accuracy of surface defects on small targets, we simplified the model structure by pruning some network feature layers and modules and added an additional small target detection layer. Second, we developed a new lightweight module, C2Fast_CA, to replace some of the C2f modules in the model to reduce parameters. Afterward, to further simplify the model structure and reduce computational complexity, we made exploratory adjustments to the reg_max in the model to find the minimum value that is most suitable for lightweight models, thereby achieving model miniaturization. Finally, we also optimize the loss function to improve the overall performance of the model in handling various difficult samples. The experimental results show that SF-Yolov8n performs better than other mainstream detection models in detecting surface defects of dental nails and achieves the highest technique for order preference by similarity to an ideal solution (TOPSIS) score. In addition, the parameter quantity of SF-Yolov8n is only 0.69 M, which is a reduction of 77.01% compared with Yolov8n. Meanwhile, the including precision (P), recall (R), and mAP50 of SF-Yolov8n are increased by 3.7%, 3.4%, and 5.8%, respectively, compared with Yolov8n.