基于改进YOLO11n模型的自动驾驶道路交通检测算法研究

doi:10.16088/j.issn.1001-6600.2024122304

摘要/Abstract

摘要： 随着自动驾驶技术的快速发展,道路交通检测作为感知模块的核心任务,直接影响自动驾驶系统的安全性与可靠性,当前基于深度学习方法作为研究的热点,却仍存在检测精度低、模型泛化性差等问题。为解决这些问题,本文提出一种改进YOLO11n模型的道路交通检测方法。该方法通过增加小目标检测层提高对小目标的检测精度,引入GhostConv+DWConv检测头结构,组合优化现有的双DWConv结构,并设计更适合小目标的Inner-CIoU损失函数,增强模型的泛化性,提高边界框回归的准确性。实验结果显示,与现有YOLO11n算法相比,该模型在KITTI和BDD100K数据集上的检测精度分别提升1.1个百分点和1.9个百分点,并达到125 帧/s和124 帧/s的检测速度,提升了低分辨率小目标检测的有效性,在不同交通场景下具有良好的泛化性。

关键词: 自动驾驶, 小目标检测, YOLO11, 多尺度检测, 损失函数

Abstract: With the rapid development of autonomous driving technology, road traffic detection, as a core task of the perception module, directly impacts the safety and reliability of autonomous driving systems. Although deep learning-based methods have become a research hotspot, challenges such as low detection accuracy and poor model generalization remain. To address these issues, this paper proposes an improved YOLO11n-based road traffic detection method. The proposed approach enhances the detection accuracy of small objects by adding a small object detection layer, optimizes the existing dual DWConv structure by introducing a GhostConv+DWConv detection head combination, and designs an Inner-CIoU loss function better suited for small objects to improve model generalization and the accuracy of bounding box regression. Experimental results show that, compared with the existing YOLO11n algorithm, the proposed model achieves detection accuracy improvements of 1.1% and 1.9% on the KITTI and BDD100K datasets, respectively, with detection speeds of 125 FPS and 124 FPS. This demonstrates the model’s effectiveness in detecting low-resolution small objects and its strong generalization capability across diverse traffic scenarios.

Key words: autonomous driving, small target detection, YOLO11, multi-scale detection, loss function

中图分类号: TP391.41;U463.6

田晟, 赵凯龙, 苗佳霖. 基于改进YOLO11n模型的自动驾驶道路交通检测算法研究[J]. 广西师范大学学报（自然科学版）, 2026, 44(1): 1-9.

TIAN Sheng, ZHAO Kailong, MIAO Jialin. Research on Automatic Driving Road Traffic Detection Algorithm Based on Improved YOLO11n Model[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 1-9.

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