Accurate detection of key traffic elements in complex road scenes is critical for autonomous driving and intelligent transportation systems. However, existing lightweight detectors often suffer from missed detections under small targets, large-scale variations, and cluttered backgrounds. To address these challenges, we propose DSE-YOLO11, a RAD-oriented lightweight adaptation of YOLO11n that integrates DynamicConv, SlimNeck, and EMA in a stage-wise collaborative manner. The main contribution lies not in introducing entirely new primitive modules, but in developing a task-specific integration strategy for improving detection robustness in complex road scenes. Specifically, a dynamic convolution-based backbone improves local feature modeling and representation of irregular and small-scale targets. A lightweight neck strengthens cross-scale feature interaction while reducing redundant fusion overhead. Additionally, an efficient attention mechanism suppresses background interference and enhances responses to key regions. Experiments on the RAD dataset show that DSE-YOLO11 improves recall from 0.744 to 0.811 and mAP50 from 0.810 to 0.856, while maintaining 2.96M parameters and 7.1 GFLOPs. These gains are practically meaningful because they indicate fewer missed detections of small, low-contrast, and safety-relevant traffic elements in complex road scenes. Additional experiments on BDD100K provide preliminary external support, although broader validation is still needed.
Chen et al. (Mon,) studied this question.