# A Deep Learning-Based Algorithm for Ceramic Product Defect Detection
## 4 Conclusions  
This study proposes a deep learning-based algorithm for ceramic product defect detection, achieving efficient and accurate detection of surface defects on ceramic products by combining the lightweight YOLOv10s model with the adaptive Canny algorithm. The algorithm operates in two stages: first, it utilizes the lightweight YOLOv10s model for preliminary defect detection, accurately identifying and locating four types of defects--cracks, glaze missing, damage, and black spots--and delineating the ROI; subsequently, the adaptive Canny algorithm is applied within the ROI to capture and locate crack defect edges with high precision. The experimental results indicate that the lightweight YOLOv10s model effectively reduces model complexity and size while maintaining high detection accuracy, achieving an mAP@50 of 92.8% and an F1-score of 90.3%. The adaptive Canny algorithm performs exceptionally well in crack detection, with a 25% reduction in ELE, a 15% increase in ECR, and a 17% improvement in WER, while achieving a frame rate of 40 fps, meeting the requirements for real-time detection.  
Despite the significant progress made in this study on ceramic product defect detection, several limitations still exist. Firstly, the dataset size is relatively limited, primarily focusing on ceramic crafts with relatively uniform colors and shapes, which may affect the model's performance when dealing with more complex handicrafts. Secondly, although the adaptive Canny algorithm performs well in crack detection, its edge detection performance for other types of defects has not been fully validated, and different defect types may require targeted adjustments to the algorithm parameters. Lastly, although the algorithm meets the requirements for real-time detection in terms of frame rate, under extreme conditions (such as high-resolution images, complex backgrounds, etc.), real-time performance may face challenges.  
In response to the above limitations, future research can be conducted from the following aspects: firstly, expand the scale and diversity of the dataset by collecting more samples of ceramic products with complex colors and shapes and introducing data augmentation techniques to increase dataset diversity; secondly, enhance the algorithm's generality by designing specialized edge detection strategies or adjusting algorithm parameters for different types of defects, and explore multi-task learning frameworks to achieve joint optimization of defect type identification and edge detection; thirdly, optimize real-time performance by improving the model's real-time processing capabilities through algorithm optimization, hardware acceleration, and other means.  
Conceptualization, J.D. and Y.Z.; methodology, J.D. and Z.D.; formal analysis, Z.D.; investigation, H.W.; resources, J.D.; writing--original draft, J.D.; writing--review and editing, Z.D.; supervision, H.W. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the 2024 Annual Project of Henan Provincial Philosophy and Social Sciences Planning-Research on the \"Site-Specific\" Design Strategies of Public Art Based on the Cultural Construction of Rural Areas in the Central Plains (Project No.: 2024 BY5019), and the 2022 Annual Project of Henan Provincial Philosophy and Social Sciences Planning-Research on the Development Paths of Public Art in Henan's Revolutionary Cultural Tourism (Project No.: 2022 BY5045).  
Not applicable.  
The data presented in this study are available on request from the corresponding author (the data are not publicly available due to privacy or ethical restrictions).  
Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.