# A Deep Learning-Based Algorithm for Ceramic Product Defect Detection
## 1 Introduction
proposed a fuzzy inpainting network and a multi-scale contrast enhancement algorithm for low-contrast defects in 3D-printed ceramic curved parts, combined with the ECANet-Mobilenet SSD model, significantly improving the recognition accuracy of cracks and protrusions [25]. [PERSON] et al. constructed a high-resolution CT surface defect dataset, improved YOLOv5, and introduced a dynamic attention mechanism and a balanced loss function, increasing detection accuracy by 6% and alleviating the problems of small targets and class imbalance [26]. [PERSON] et al. proposed an improved edge-detection algorithm based on cluster analysis to enhance the accuracy of crack repair in ancient ceramics. This algorithm utilizes cluster analysis to optimize the Sobel operator and subsequently alters the gray-level distribution of the edge-detection map. Experiments demonstrate a 20% improvement in the alignment of the detected contour crack trajectories with the edge directions [27]. [PERSON] et al. first employed the Otsu method to extract the detection regions within the image. Then, they combined the Canny algorithm with methods like the Fast Fourier Transform (FFT) to detect defects such as pits, bubbles, and protrusions on the ceramic surface. The system achieved a detection accuracy of over 97.2% [28]. As shown in Table 1, existing methods such as manual visual inspection, traditional machine vision-based inspection, object detection algorithms, and semantic segmentation algorithms have all played their respective roles in the field of ceramic product defect detection. However, they also come with some notable limitations. For instance, manual visual inspection is inefficient and has poor data traceability. Traditional machine vision has limited functionality and is susceptible to environmental interference. Although object detection algorithms boast strong real-time performance, they have high model complexity and resource requirements. Semantic segmentation algorithms can provide fine-grained boundary information but suffer from a large computational load and poor real-time performance. In addition, these methods often struggle to simultaneously meet various demands such as detection accuracy, efficiency, and adaptability. Furthermore, in actual production applications, ceramic products have a wide variety and complex types of defects, placing even stricter requirements on inspection technologies. To address these challenges, this study proposes a deep-learning-based ceramic product defect detection algorithm. The main workflow is illustrated in Figure 1. First, the algorithm utilizes a lightweight YOLOv10s detector to process the input images. It is used to identify and locate four common types of defects: cracks, glaze deficiency, breakage, and black spots. At the same time, it designates the defect areas containing cracks as regions of interest (ROIs). Subsequently, within the ROIs, an adaptive Canny detection technique is employed to accurately capture and locate the edges of cracks, thereby providing strong support for subsequent process improvements. The structure of this paper is arranged as follows: Chapter One elaborates on the importance of defect detection in ceramic products and the limitations of existing detection methods, thereby presenting the research background and significance of a deep-learning-based algorithm for ceramic product defect detection. Chapter Two provides a detailed introduction to the constructed ceramic product defect dataset and its building process, encompassing aspects such as data collection, annotation, and validation. Chapter Three describes the ceramic product defect detection algorithm based on the lightweight YOLOv10s model, with a focus on introducing the model's design, optimization, and its performance in defect detection tasks. Chapter Four proposes a ceramic crack detection method based on an adaptive Canny edge detection algorithm. The superiority of this method in crack edge detection is verified through experiments.