To address the problem that existing algorithms cannot accurately perform the detection task of tire side pressure marking text area due to interference from dark background, this paper proposes a tire side pressure marking text area detection algorithm TireYOLO based on the improved YOLOv8s. Firstly, a lightweight feature extraction module RepNCSPELAN4_CAA is proposed to replace the C2f module in the main body network of YOLOv8s, which can capture contextual feature information better through the CAA attention mechanism and improve model accuracy while reducing computational cost. Secondly, in order to better fuse features while keeping lightweight, a dynamic sampling lightweight large receptive field feature fusion neck structure DyLKVHSPAN is adopted, and an efficient feature fusion network HSPAN is proposed by improving the HSFPN network structure. At the same time, a large receptive field feature fusion module UniLKVoVCSP is proposed to dynamically sample features through the Dysample upsampling operator, so that DyLKVHSPAN can fuse features better with large receptive field while keeping lightweight. Finally, a low computational cost detection head DWLHead is proposed, which redesigns the structure of the original detection head to improve detection performance while reducing computational cost. Experimental results show that compared with YOLOv8s, the TireYOLO algorithm improves the mAP0.5:0.95 of the key information area detection in tire side by 2.5 percentage points, reduces the number of parameters by 48.4%, and reduces the computational cost by 49.6%, verifying the superiority of the proposed algorithm in detecting tire side text area.