# A Deep Learning-Based Algorithm for Ceramic Product Defect Detection
## 3 Experiments and Results
### Ceramic Defect Detection Model Based on Lightweight YOLOv10s
#### 3.1.2 Ablation Experiments  
To validate the effectiveness of the proposed lightweight YOLOv10s model and its improved modules in the task of ceramic defect detection, we conducted a series of ablation experiments using an existing YOLO dataset. The experimental results are detailed in Table 3.  
As indicated by the data in Table 3, the original YOLOv10s model demonstrates exceptional performance in the task of ceramic product defect detection, achieving an mAP@50 of 92.5% and an F1-score of 90.1%. When the lightweight backbone network improvement scheme based on GhostConv is adopted, although there is a slight decline in detection accuracy (with mAP@50 decreasing by 1.9% and F1-score by 2.6%), the lightweight effect is remarkable; the model parameters (Params) are reduced by 24.2%, the computational load (GFLOPs) is lowered by 37.1%, and the model size (Size) is shrunk by 25.6%. This outcome suggests that the backbone network improvement based on GhostConv is feasible for lightweight optimization of ceramic defect detection models, effectively reducing model complexity and size while maintaining relatively high detection accuracy. During the improvement of the PSA module, by introducing the ECA mechanism, the model's complexity metrics are further optimized while the accuracy metrics remain largely stable. After further optimizing the ECA mechanism and appropriately increasing the model complexity, the detection accuracy is significantly enhanced, with mAP@50 rising to 92.8% and F1-score increasing to 90.3%. This improvement validates the importance of enhancing the model's multi-scale feature extraction capability for improving the performance of complex defect detection. Overall, through the collaborative optimization of the GhostConv lightweight backbone network and the ECA-PSA module, the improved model achieves a more efficient lightweight design while maintaining detection accuracy. Compared to the original model, the lightweight YOLOv10s model sees an increase of 0.3% in mAP@50 and 0.2% in F1-score, along with reductions of 26%, 37.9%, and 26.8% in Params, GFLOPs, and Size, respectively. This achievement provides a solid foundation for subsequent ceramic defect edge detection tasks.  
As illustrated in Figure 7, the defect detection performance in ceramic products based on the lightweight YOLOv10s model is visually demonstrated. From the visual analysis results of the test images, the model exhibits a high degree of sensitivity to various types  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **mAP@50 (\%)** & **F1-Score (\%)** & **Params (M)** & **GFLOPs (G)** & **Size (MB)** \\ \hline YOLOv10s & 92.5 & 90.1 & 8.07 & 24. & 16.8 \\ +GhostConv & 90.6 & 87.5 & 6.12 & 15.1 & 12.5 \\ +GhostConv + ECA & 90.5 & 87.7 & 5.66 & 14.6 & 12.1 \\ +GhostConv + Improved ECA & 92.8 & 90.3 & 5.97 & 14.9 & 12.3 \\ (Lightweight YOLOv10s) & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments.  
of defects on ceramic surfaces, including cracks, glaze missing, damage, and black spots. Among these, black spots and glaze-missing defects, due to their distinct contrast, can be easily and accurately detected by the model. When faced with more complex defects like cracks and damage, the model also performs exceptionally well. It not only reliably detects the presence of these defects but also provides precise boundary localization. Whether it is large-scale cracks or minute damage, the lightweight YOLOv10s model can accurately locate them. This showcases the model's multi-scale detection capability, enabling it to adapt to defect detection needs of varying sizes and shapes, thereby enhancing the accuracy and reliability of detection. Additionally, the lightweight YOLOv10s model demonstrates strong stability in detecting ceramic product defects under different lighting conditions, effectively coping with complex lighting variations caused by factors such as natural light and workshop illumination in ceramic production and inspection environments.