International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/2418 2020 2020 School of Graduates Studies of Management and Science University, Selangor, Malaysia; Mianyang Polytechnic, Mianyang, Sichuan, China Li .. Software Engineering and Digital Innovation Center, Management and Science University, Selangor, Malaysia Muhammad Irsyad Abdullah To achieve automation of defect detection, the metal surface micro defect detection algorithm YOLO-MD is proposed. From the perspective of object detection, YOLOv5s is selected as the backbone algorithm and the SPD-Conv module is added to reduce feature loss caused by ordinary convolutional downsampling, improve the adaptability of low-resolution images, and improve the accuracy of small object detection. Using the MPDIoU loss function to accelerate model convergence and improve detection accuracy. Considering the small size of the dataset, data augmentation methods were adopted. After model training, mAP50-95 improved by 0.02 compared to YOLOv5, which has high real-time and robustness and can more effectively detect metal surface micro defects. 2024 2024 308 316 10.54216/IJNS.230225 https://www.americaspg.com/articleinfo/21/show/2418