To address the challenges of low detection accuracy for small-target vehicles/pedestrians and high computational complexity in autonomous driving scenarios, this paper proposes DSWR-YOLO—an improved lightweight object detection algorithm based on YOLOv10n. First, a DWR module is introduced, where its dilated residual structure is enhanced to DSConv. The SimAM attention mechanism is embedded to reconstruct the C2f module, strengthening feature retention for small targets. Second, an additional 160×160 detection layer is incorporated. A novel dynamic detection head, Detect-dyHead-P2, is designed using multi-expert fusion and separation combined with DynamicConv, significantly reducing model parameters while improving small-target detection capability. Finally, the Focaler-SDIoU loss function is integrated to dynamically adjust loss weights, mitigating sample imbalance and unstable bounding box regression.Validation on the VisDrone2019 dataset demonstrates that DSWR-YOLO achieves a 25.9% reduction in parameters and 33.3% decrease in FLOPs, while improving mAP@0.5 by 3.7%, Precision by 2.9%, and Recall by 3.3%. This delivers enhanced accuracy with reduced computational costs, making it suitable for resourceconstrained embedded devices. Generalization experiments on the UA-DETRAC dataset show that DSWR-YOLO outperforms the baseline by 0.9% in mAP@0.5, 1.2% in Precision, and 2.0% in Recall, confirming its robust generalization capability.