Small object detection is challenging, especially in complex traffic scenarios with varied backgrounds and multi-scale issues. This paper proposes an enhanced DM-YOLOv10n model, integrating Deformable Convolutional Network v3 (DCNv3) and Multi-dimensional Channel Attention (MCA) to improve detection accuracy. Key improvements include embedding DCNv3 into C2f modules of the backbone network to enhance small object feature extraction, adding a P2 detection head alongside P3, P4, and P5 to capture fine details from high-resolution feature maps, and incorporating MCA to better focus on important features in complex backgrounds. The proposed method has a 7.0% improvement in mean Average Precision (mAP), reaching 90.1%, along with a 5.3% improvement in precision and a 9.0% increase in recall. Experimental results on a self-constructed traffic dataset demonstrate the model's enhanced performance in detecting small objects.