To address the challenges of high computational overhead and insufficient detection capability for small and deformed targets when deploying existing traffic sign detection algorithms on edge computing devices, this paper proposes a lightweight YOLOv8-based traffic sign detection algorithm LTSYOLO incorporating channel pruning. First, building upon YOLOv8n, we design a feature fusion module combining noise suppression and deep semantic enhancement (NSSE) to optimize multi-scale feature representation and suppress background interference. Second, we introduce a multi-scale channel attention (MSCA) mechanism into the backbone network and integrate local deformation attention (LDA) before the detection head, enhancing the model′s perception of multi-scale targets and robustness to geometric deformations, thereby resulting in a high-precision model, TS-YOLO. Finally, to achieve model compression, we apply a BatchNorm scaling factor-based channel pruning strategy to compress TS-YOLO, obtaining the final LTS-YOLO model. Experimental results on the TT100K and CCTSDB datasets demonstrate that, compared to the baseline YOLOv8n, TS-YOLO improves mAP@50 by 2.5% and 1.8% on TT100K and CCTSDB, respectively. After pruning, the resulting LTS-YOLO model maintains its accuracy advantage while significantly reducing both parameter count and computational complexity, demonstrating the effectiveness and practicality of the proposed method.