# A Deep Learning-Based Algorithm for Ceramic Product Defect Detection
## 1 Introduction  
Ceramic products, as widely used materials in daily life, have a surface quality whose importance cannot be overstated. It not only affects the aesthetic appeal of the products but is also closely tied to their performance and service life, thereby profoundly influencing the overall quality, operational safety, and market competitiveness of the products. However, during the production and transportation of ceramic products, various factors such as fluctuations in process parameters, collisions during handling, and changes in environmental temperature and humidity can lead to the appearance of various defects on the ceramic surface, including cracks, glaze missing, damage, and black spots. These defects not only significantly reduce the product's yield rate but may also pose safety hazards during use. Traditional methods for detecting defects in ceramic products primarily rely on manual visual inspection. However, this approach has numerous limitations, such as low detection efficiency, high susceptibility to subjective factors, and difficulties in data traceability, making it difficult to meet the modern manufacturing industry's demands for high precision, real-time performance, and data-driven quality improvement [1]. Thus,achieving efficient and accurate detection of defects in ceramic products is of utmost importance for enhancing product quality, optimizing production processes, and reducing losses during transportation [2; 3]. In the field of computer vision, deep learning technology is employed to precisely locate and classify specific targets within images or videos, primarily divided into object detection-based methods and semantic segmentation-based methods [4; 5]. Among them, object detection methods leverage the powerful feature extraction capabilities of deep convolutional neural networks (CNNs) to perform multi-scale and multi-level feature learning on input images [6]. These network architectures, such as the classic Faster R-CNN [7], YOLO [8] series, and SSD [9], can automatically learn low-level texture features, mid-level shape features, and high-level semantic features from images, thereby capturing the essential characteristics of targets under varying scales, poses, and lighting conditions. They are widely applied in fields such as pest and disease diagnosis [10], crop recognition [11], defect detection [12], and intelligent traffic monitoring [13]. On the other hand, semantic segmentation-based methods focus on assigning each pixel in an image to a specific category, enabling fine-grained understanding of images at the pixel level [14]. These network architectures, such as UNet [15], DeepLab [16] series, and SegNet [17], can capture rich spatial contextual features, fine boundary information, and complex relationships between different semantic categories within images. With the development of deep learning technology, they have been widely applied in fields such as medical image analysis [18], remote sensing image interpretation [19], visual navigation [20], and plant detection [21]. In recent years, deep learning technology has provided solid technical support for the field of ceramic product defect detection due to its high precision, strong real-time performance, and excellent adaptability to complex scenarios. [PERSON] et al. proposed a lightweight model based on an improved YOLOv5s for detecting small defects on tile surfaces. By deepening the backbone network, introducing the CBAM attention module, and designing a four-output prediction layer, they enhanced feature extraction and detection efficiency [22]. [PERSON] et al. focused on small defects in high-resolution tile images, optimizing the YOLOv5s bottleneck module, adopting a Shufflenetv2 backbone, and combining it with multi-sliding window detection, achieving breakthroughs in model lightweighting and detection speed [23]. [PERSON] et al. utilized acoustic noise data and transfer learning to construct a model for high-precision detection of invisible internal cracks in ceramics [24]. [PERSON] et al.