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DSE-YOLO11: Dynamic feature adaptation for key traffic element detection in complex road scenes.

Yange Chen¹, Baohua Guo^1,2, David Bassir^3,4

¹School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo, Henan, China.

|June 1, 2026

Summary

DSE-YOLO11 enhances traffic element detection in complex road scenes by improving recall and mAP50. This lightweight model reduces missed detections of small, safety-critical traffic elements for autonomous driving.

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Area of Science:

Computer Vision
Artificial Intelligence
Autonomous Systems

Background:

Accurate detection of traffic elements is crucial for autonomous driving and intelligent transportation systems.
Lightweight detectors struggle with small targets, scale variations, and cluttered backgrounds in complex road scenes.

Purpose of the Study:

To propose DSE-YOLO11, a lightweight object detection model for robust traffic element detection in complex road scenes.
To improve the detection of small, irregular, and safety-relevant traffic elements.

Main Methods:

Developed DSE-YOLO11, a RAD-oriented adaptation of YOLO11n, integrating DynamicConv, SlimNeck, and Efficient Attention Mechanism (EMA).
Employed a dynamic convolution-based backbone for enhanced local feature modeling.
Utilized a lightweight neck for efficient cross-scale feature interaction.
Incorporated an attention mechanism to reduce background interference.

Main Results:

DSE-YOLO11 improved recall from 0.744 to 0.811 and mAP50 from 0.810 to 0.856 on the RAD dataset.
The model maintains a low parameter count (2.96M) and computational cost (7.1 GFLOPs).
Demonstrated practical significance by reducing missed detections of critical traffic elements.

Conclusions:

DSE-YOLO11 offers a robust and efficient solution for traffic element detection in challenging road environments.
The proposed integration strategy enhances detection performance without significant increases in model complexity.
Preliminary validation on BDD100K suggests broader applicability, warranting further research.