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Infrared traffic object detection faces challenges such as low resolution, weak thermal 2 contrast, and inefficiency in detecting small objects. To address these issues, this paper 3 proposes RES-YOLO, an enhanced YOLOv8n-based architecture. It incorporates Receptive 4 Field Adaptive Convolution for improved multi-scale perception, Efficient Multi-scale 5 Attention for better feature representation, and the Scylla-IoU loss for more accurate 6 and faster bounding box regression. Additionally, a pseudo-color infrared dataset is 7 constructed to enrich texture and contrast information beyond conventional white-hot 8 images. Experiments on both the FLIR public dataset and a self-built dataset show RES- 9 YOLO improves accuracy by 4.9% and 5.5% over the baseline while maintaining real-time 10 performance. These results highlight the method’s effectiveness in integrating lightweight 11 deep learning and dataset enhancement for robust perception in intelligent vehicle systems, 12 supporting AI-driven autonomous driving and driver assistance applications.