<p>Traffic object detection in complex scenarios frequently requires adaptation to varying illumination and weather conditions, while also facing challenges such as small objects, occlusions, and blurring. These demands impose higher requirements on the precision and robustness of object detectors. To address this, this paper improves the YOLO11n object detection algorithm and proposes the CTS-YOLO model. The main innovations and contributions of this algorithm include: the iRMA detection layer, which is a novel integration of EMA attention and an inverted residual structure specifically designed for small object detection; the SaEPPF module, a channel-aware multi-scale pooling mechanism; the SAC3k2 module, which enables dynamic multi-scale feature fusion; and the Inner-ShapeloU loss for shape-aware regression. Through the incorporation of these enhanced modules, the algorithm achieves a balance between improved accuracy and real-time performance. Experimental results demonstrate that CTS-YOLO outperforms baseline models on both the BDD100K and KITTI datasets, improving mAP@50 by 7.8% and 3.2%, respectively. Additionally, it achieves a real-time detection speed of 137 FPS on BDD100K, ensuring real-time performance.</p>