To address the problem of insufficient accuracy in LCD Mura defect detection due to low contrast and diverse scale differences, from the perspective of improving the model’s performance in detecting small-scale defects and weak defects, an improved YOLOv8n-based LCD Mura defect detection model, YOLO-D3MNet, is proposed. Firstly, the backbone and neck networks of the model are reconstructed through the introduction of the ConvNeXtv2 module, which improves the weak feature extraction capability of the model under the background of complex texture. Secondly, for the problem of insufficient cross-channel communication of feature information in the detection head module, an efficient decoupling head combining the channel shuffle strategy and depth-separable convolution is proposed to promote the information flow between different feature channels and reduce the model computation power requirement. Finally, to address the problem that the intersection and concatenation ratio metric based on prediction box and truth box is sensitive to the positional bias of small-scale defects, the normalized Gaussian Wasserstein distance loss function is introduced to provide more positive sample candidate boxes, which improves the model’s detection performance of Mura defects. The precision, recall and mAP₅₀ of the improved YOLO-D3MNet model are 92.9%, 88.8% and 94.8%, respectively. Compared to the base model YOLOv8n, the precision, recall and mAP₅₀ of the YOLO-D3MNet model are improved by 3.4%, 2.7% and 3.6%, respectively, while the GFLOPs of the model are reduced by 24.7%. Compared with mainstream target detection models such as YOLOv5n, the experimental results show that the YOLO-D3MNet model proposed in this paper has better performance in LCD Mura defect detection.