EMN-Net: A Lightweight YOLOv8-Based Model for Real-Time Surface Defect Detection of Pharmaceutical Tablets

Continuous manufacturing has emerged as the prevailing paradigm in the modern pharmaceutical industry, imposing stringent demands for efficient, real-time inspection methods. Furthermore, deploying high-performance deep learning models on industrial edge devices remains challenging due to computational constraints and the difficulty of detecting micro-defects (e.g., micro-cracks and spots). This paper proposes EMN-net, a lightweight defect detection model built upon the YOLOv8n architecture. The proposed algorithm integrates a MobileNetV3 backbone, the Efficient Local Attention (ELA) mechanism and the Normalized Wasserstein Distance (NWD) loss function to balance computational efficiency with sensitivity toward micro-defects. Evaluated on a self-built industrial tablet dataset expanded to 3086 images, EMN-net achieves an mAP50 of 97.8%, representing a 2.5% improvement over the baseline YOLOv8n. the computational complexity is reduced to 4.4 GFLOPs, while the inference throughput reaches 118 FPS, satisfying the real-time requirements of high-speed production lines. Additionally, the model exhibits improved robustness under simulated motion blur and sensor noise. EMN-net presents a balanced automated visual inspection solution for edge devices in continuous pharmaceutical manufacturing.