In order to solve the problems such as the large amount of YOLOv8 parameters affecting the detection speed, this paper proposes a lightweight leather defect detection algorithm based on the YOLOv8 framework by using automotive seat leather as a sample for defect detection on the surface of automotive seats. Firstly, the original backbone network of YOLOv8 is replaced with the lightweight network StarNet, which achieves the mapping of high-dimensional and nonlinear feature spaces through star arithmetic, thus demonstrating impressive performance and low latency with a compact network structure and low energy consumption. Second, the original detection head is replaced with a lightweight shared convolutional detection head (LSCD), which allows for a significant reduction in the number of parameters through the use of shared convolution, making the model lighter so that it can be easily deployed on resource-constrained devices. Finally, the C2f module of the neck network is replaced by the C2f_Star module, which fuses feature maps of different scales while the network is more lightweight to improve the accuracy and robustness of target detection. Experimental validation of the model on the home-made HSV-Leather dataset shows that the improved YOLOv8-Leather detection model outperforms the YOLOv8n model. Compared to the YOLOv8n model, the improved model reduces the number of parameters by 57%, improves the detection speed by 20%, reduces the model weights by 52%, and reduces the computation by 53%. The experiment verifies the feasibility of the improved model in solving the problem of leather surface defect detection.