Abstract: Abstract: Aiming at the problem of low detection accuracy and slow detection speed of ceramic tile defect detection caused by small defects and complex patterns on the surface of ceramic tiles， a ceramic tile surface defect detection algorithm based on improved YOLOv8 is proposed. The algorithm designs a spatial pooling layer module LSPPF incorporating the LSKA （Large Separable Kernel Attention） mechanism to improve the multi-scale feature extraction capability of the model； a lightweight feature fusion network SimBiFPN is designed to reduce the number of jump connections in the weighted bidirectional feature pyramid network， enhance the model’s representation of small target defect features， and reduce the number of model parameters; a D-Slim-Neck module based on lightweight convolution GSConv （Grouped Shuffle Convolution） and depth-separable convolution （Depthwise Separable Convolution， DSConv） is proposed for replacing the traditional Slim-Neck module to significantly reduce the model computation and complexity; to address the problem of different shapes of tile surface defects， a loss function Inner_CIoU with an auxiliary box is proposed to control the size of the auxiliary frame by adjusting the scale factor to further improve the detection accuracy of the algorithm. The experimental results show that the mean average precision of the improved algorithm reaches 90.3%， which is 2.1 percentage points higher than YOLOv8， and the number of parameters and calculations are reduced by 6.7% and 13.6% respectively， achieving tile surface defect detection performance comparable to other advanced algorithms.

Key words: Key words: ceramic tile defect detection, YOLOv8, large kernel attention mechanism, multi-scale feature fusion, GSConv