In response to the challenges posed by the weak characteristics, diverse types, and high similarity with the background of surface defects in liquid crystal displays (LCDs), which result in low detection accuracy with existing methods, this paper proposes an improved micro-defect detection model for LCDs based on YOLOv10, referred to as LC-YOLO. First, the convolutional module in the neck network is replaced with a full-dimensional dynamic convolution (ODConv), which reduces the computational load of the model while maintaining detection accuracy and improving the precise extraction of small defect features. Next, to further optimize the model′s performance, the DySample dynamic upsampling module is introduced. This module avoids background interference by point sampling, thereby reducing false positives and false negatives, and enhancing the model′s robustness in complex backgrounds. Finally, to enhance the model′s ability to detect small targets, the EMAttention attention mechanism is incorporated. This mechanism improves the model′s sensitivity to small and dim targets, significantly boosting overall performance. Experimental results on a dataset of 1,774 images containing three types of defects—scratches, corner breaks, and dents—demonstrate that compared to the original YOLOv10 model, LC-YOLO improves mean average precision,accuracy rate, and recall by 2.9%, 2.4%, and 5.8%. Meanwhile, the computational load of the model is reduced by 2%. When compared to existing object detection algorithms, LC-YOLO not only retains its lightweight characteristics but also enhances detection accuracy and speed, showing excellent performance in detecting subtle surface defects in LCDs.