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| GEIT-YOLO模型在智能驾驶中的研究与应用 |
| Research and Application of GEIT-YOLO Model in Intelligent Driving |
| 投稿时间：2025-09-24 |
| DOI：10.16018/j.cnki.cn32-1650/n.202601006 |
| 中文关键词: 目标检测 复杂行驶环境 智能驾驶 GEIT-YOLO 检测头 |
| 英文关键词: object detection complex driving environments intelligent driving GEIT-YOLO detection head |
| 基金项目:安徽省高校自然科学研究重点项目(2022AH052821) |
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| 中文摘要: |
| 为解决传统YOLO模型在复杂道路行驶环境中性能差等问题,提出一种基于改进YOLO11n的智能驾驶目标检测模型GEIT-YOLO。为了让边缘信息融合到各个尺度所提取的特征中,提出GEIT模块,其能够把浅层特征中提取到的边缘信息传递到整个backbone上,并与不同尺度的特征进行融合;同时引入门控注意力机制CGLU,通过门控特征调制联合建模通道依赖与空间上下文关系;最后引入LQE检测头通过显式地评估每个边界框的定位质量,优化NMS阶段并且优先选择更高质量的边界框。实验结果表明:与YOLO11n相比,本文所提出的模型GEIT-YOLO在Udacity数据集上Precision、Recall、mAP50、mAP50:95分别提升了5.6%、2.7%、4.2%、2.3%,改进模型在复杂驾驶环境中的表现更加优异,能显著提升模型检测能力。 |
| 英文摘要: |
| To address issues such as the poor performance of traditional YOLO models in complex road environments, this paper proposes GEIT-YOLO, an object detection model for intelligent driving based on an improved YOLO11n. To enable the integration of edge information into features extracted at various scales, the GEIT module is proposed. It propagates the edge information extracted from shallow features throughout the entire backbone network and fuses it with features at different scales. Concurrently, the CGLU gated attention mechanism is introduced to jointly model channel dependencies and spatial contextual relationships through gated feature modulation. Finally, the LQE detection head is incorporated to explicitly evaluate the localization quality of each bounding box, thereby optimizing the Non-Maximum Suppression (NMS) stage and prioritizing bounding boxes of higher quality. Experimental results show that, compared to YOLO11n, the proposed GEIT-YOLO model achieves improvements of 5.6% in Precision, 2.7% in Recall, 4.2% in mAP50, and 2.3% in mAP50:95 on the Udacity dataset. The improved model demonstrates superior performance in complex driving environments and can significantly enhance detection capability. |
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