To address the demands for real-time and high-precision Micro LED defect detection， this study introduces LED-YOLO， a rapid and accurate detection algorithm that integrates a lightweight architecture with enhanced feature extraction capabilities. An image acquisition system was designed to simulate industrial interference， and various data augmentation techniques were employed to increase the diversity of training data. To overcome the limited discriminative power for Micro LED defects， a Lightweight Dynamic Fusion Module （LDFM） was developed， combining dynamic convolution， deep convolution， and channel mixing operations； this approach maintains model compactness while enhancing feature extraction. Furthermore， an Enhanced Coordinated Attention Module （ECAM） was proposed to improve defect localization by integrating channel and spatial attention mechanisms alongside residual connections， thus refining feature extraction accuracy. Given the minimal aspect ratio variation in Micro LED images， a dynamic focusing mechanism was incorporated， and a DIoU_W regression loss function was introduced to accelerate convergence and improve robustness. Experimental results demonstrate that LED-YOLO surpasses the state-of-the-art YOLOv11s in detection accuracy， recall， mean average precision （mAP）， and F1 score. Despite a reduction of 1.6 million parameters， LED-YOLO achieves substantial improvements in detection speed and accuracy， effectively fulfilling the quality inspection requirements of Micro LED panel manufacturing.