In the field of ceramic product defect detection, traditional manual visual inspection methods suffer from low efficiency and high subjectivity, while existing deep learning algorithms are limited in detection efficiency due to their high complexity. To address these challenges, this study proposes a deep learning-based algorithm for ceramic product defect detection. The algorithm designs a lightweight YOLOv10s detector, which reconstructs the backbone network using GhostNet and incorporates an Efficient Channel Attention (ECA) mechanism fused with depthwise separable convolutions, effectively reducing the model’s complexity and computational load. Additionally, an adaptive threshold method is proposed to improve the traditional Canny edge detection algorithm, significantly enhancing its accuracy in defect edge detection. Experimental results demonstrate that the algorithm achieves an mAP@50 of 92.8% and an F1-score of 90.3% in ceramic product defect detection tasks, accurately identifying and locating four types of defects: cracks, glaze missing, damage, and black spots. In crack detection, the average Edge Localization Error (ELE) is reduced by 25%, the Edge Connectivity Rate (ECR) is increased by 15%, the Weak Edge Responsiveness (WER) is improved by 17%, and the frame rate reaches 40 frames per second (f/s), meeting real-time detection requirements. This algorithm exhibits significant potential in the field of ceramic product defect detection, providing solid technical support for optimizing the ceramic product manufacturing process.