To solve the insufficient detection accuracy of the YOLO model and the numerous parameters required for detecting defects in cylindrical honeycomb ceramic images, a lightweight, cylindrical honeycomb ceramic defect-detection algorithm based on the improved YOLOv8n is proposed. To mitigate fuzzy boundary localization for crack defects, Shape-IoU is used to optimize bounding-box regression and improve the localization accuracy via weight coefficients and shape-loss terms. Meanwhile, to enhance the recognition ability of low-resolution small target cracks, an efficient multiscale attention (EMA) mechanism is introduced to enhance the network's capture and extraction of feature information. The algorithm integrates an improved SCConv module in the backbone to reduce parameter redundancy. Based on this, a space and channel feature fusion pyramid module is designed to achieve a lightweight network model. Compared with the original network, the improved network offers a higher average prediction accuracy by 2.9 percentage points, a lower parameter count by 84.1%, and an increase in the number of frames per second by 9 frames. Additionally, the proposed model is lighter and features a smaller computational load, which is more conducive to actual model deployment and embedded use.