# A Deep Learning-Based Algorithm for Ceramic Product Defect Detection
## 3 Experiments and Results
### Ceramic Defect Detection Model Based on Lightweight YOLOv10s
#### 3.1.3 Comparative Experiments on Different Ceramic Product Defect Detection Models  
To further evaluate the performance of the lightweight YOLOv10s model in the task of ceramic product defect detection, we conducted quantitative comparative experiments on mainstream YOLO series models while keeping the dataset and hardware-software configurations completely consistent. To more intuitively reflect the model's detection speed, an additional evaluation metric, FPS (frames per second, the number of image frames the model can process per second), was included. The experimental results are detailed in Table 4.  
It is evident from the data presented in Table 4 that, when compared with other mainstream models from the YOLO series, the lightweight YOLOv10s model demonstrates the most outstanding overall performance.  
In terms of detection accuracy, the model achieves mAP@50 and F1-score metrics of 92.8% and 90.3%, respectively, significantly surpassing the other five mainstream models. Specifically, compared to the YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and YOLOv10s models, mAP@50 exceeds them by 3.7%, 4.5%, 1.7%, 2.6%, and 0.3%, respectively; F1-score surpasses them by 3.4%, 4.2%, 1.4%, 2.2%, and 0.2%, respectively. This data fully indicates that the lightweight YOLOv10s model possesses stronger defect recognition and localization capabilities in the task of ceramic product defect detection, enabling it to more accurately detect various types of defects on ceramic products.  
Figure 7: Defect detection performance based on the lightweight YOLOv10s model.  
In terms of model complexity, the lightweight YOLOv10s model's Params, GFLOPs, and Size metrics are 5.97 M, 14.9 G, and 12.3 MB, respectively, all significantly lower than those of the other five mainstream models. Compared to the YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and YOLOv10s models, Params are reduced by 14.96%, 63.42%, 52.09%, 17.43%, and 26.02%, respectively; GFLOPs are reduced by 5.70%, 58.03%, 48.08%, 44.40%, and 37.92%, respectively; and Size is reduced by 11.51%, 57.88%, 45.33%, 13.38%, and 26.79%, respectively. This implies that the lightweight YOLOv10s model, while maintaining high detection accuracy, has lower computational complexity and a smaller model size, enabling it to run more efficiently on resource-constrained devices and reducing hardware resource requirements and deployment costs.  
Furthermore, for the task of ceramic defect detection, FPS is the most critical metric, directly measuring the model's real-time processing speed, which is crucial for real-time detection in actual production. The proposed lightweight YOLOv10s model achieves an FPS of 63 frames per second, whereas most cameras operate at a rate of only 30 frames per second. Therefore, this model fully meets real-time operation standards and takes into account the potential increase in detection difficulty due to complex defects in a single frame. Compared to the YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and YOLOv10s models, FPS exceeds them by 21.15%, 90.91%, 65.79%, 53.66%, and 40.00%, respectively, showcasing far superior real-time detection capabilities compared to other models.  
Overall, the proposed lightweight YOLOv10s model not only boasts excellent detection accuracy, enabling precise recognition of ceramic product defects in complex scenarios, but also possesses the best detection speed, allowing for rapid response and handling of a large number of detection tasks. This provides strong technical support for the defect detection process of ceramic products, helping to improve production quality and efficiency, reduce defect rates, and has significant practical application value.