To address the problem of low detection accuracy of weak feature defects in LCD display defects caused by multiple convolution and background texture assimilation, an improved model YOLO-Mura for LCD weak feature defect detection based on YOLOv5 is proposed. Firstly, Involution operator is introduced in the backbone network to expand perceptual field, enhance the information of weak feature defects in spatial range, and reduce model FLOPs. Secondly, the CARAFE upsampling operator is used to optimize the upsampling method and enhance the ability to focus on weak feature defects. Then, in the neck network, the feature extraction ability of the network under strong background interference is enhanced by embedding the BiFormer attention module. Finally, the BiFPN weighted bidirectional pyramid structure is adopted to improve the feature fusion utilization at different levels. Experimental results on the homemade LCD Mura defect dataset show that the accuracy, recall, and mAP@0.5 of YOLO-Mura model are improved by 2.2%, 6.6%, and 2.7%, respectively, and the model computation is reduced by 66.5%. In comparison with the mainstream target detection algorithms, the results show that the final improved model in this paper has better detection performance for Mura defects with weak features of LCDs.