Radial tire X-ray images exhibit complex textures and diverse defect morphologies, often relying on manual visual inspection for quality control—a process that struggles to balance high precision with real-time efficiency. To address this, a detection model based on an improved version of YOLOv8, named YOLOv8n_RSI, is proposed for detecting air bubble defects in radial tires. First, the RepNCSPELAN4 architecture is introduced to enhance feature extraction capabilities. Second, the SKAttention mechanism is integrated to adaptively select receptive field sizes, improving the model′s detection performance across multiple scales. Finally, the Inner-CIoU loss function is adopted, incorporating center point distance constraints and aspect ratio penalties to effectively enhance detection accuracy. Experimental results demonstrate that compared to the baseline YOLOv8n model, the proposed YOLOv8n_RSI achieves average improvements of 3.5% in precision, 7.0% in recall, and 8.4% in mean average precision. Furthermore, the model′s computational complexity and inference speed indicate its suitability for real-time detection requirements. Preliminary industrial applications also validate the effectiveness of this improved model.