aspg-21-2418-1791468712 20261008141152 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2024 23 2 Li Jiao School of Graduates Studies of Management and Science University, Selangor, Malaysia; Mianyang Polytechnic, Mianyang, Sichuan, China Muhammad Irsyad Abdullah Software Engineering and Digital Innovation Center, Management and Science University, Selangor, Malaysia 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 308 316 2418 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.230225 https://www.americaspg.com/journal/21/article/2418 https://www.americaspg.com/storage/61705600563.pdf