Microfiber leather is a high-end composite material， and its defect detection is critical for ensuring product quality. To address the challenges posed by the multi-scale， diverse aspect ratios， and numerous small defects on the surface of microfiber leather， the MFL_YOLOv8 algorithm for surface defect detection was proposed in this study. The MFL_YOLOv8 algorithm first introduced the multi-scale feature extraction module DCNv3-LKA based on the Deformable Large Kernel Attention （DLKA） mechanism， which significantly enhanced the backbone network's multi-scale feature extraction capabilities. Subsequently， the incorporation of a P2 feature map and a Dysample upsampling module in the feature pyramid network strengthened the network's ability to extract detail information from small targets. Finally， the Minimum Points Distance Intersection over Union （MPDIoU） was utilized to mitigate the inefficacy of the loss function on small targets during the initial stages of training， thus improving the detection performance for small targets. Experimental results on a self-constructed microfiber leather surface defect dataset demonstrate that the proposed algorithm achieved 92.47% of average detection precision and 92.40% of average detection recall， with improvements of 5.38% and 7.27% compared to YOLOv8n. Additionally， the algorithm attainsed a frame rate of 135.2 frames per second （FPS）， meeting the accuracy and real-time requirements for industrial applications.