Abstract:

With the advancement of autonomous driving technology， real-time vehicle detection has become crucial for ensuring system safety and reliability. Therefore， a lightweight detection model based on YOLOv10， named YOLOv10-LITE was designed by introducing four structural improvement modules to reduce model complexity and inference latency while maintaining detection accuracy， for real-time detection tasks in resource-constrained environments. Specifically， the Dynamic Upsampling （DySample） module was applied to enhance the resolution of feature maps while reducing computational cost； the Fast Multi-Scale Network （FastMSNet） module was used to improve multi-scale feature extraction and enhance detection performance for objects of different sizes； the Spatial Pyramid Pooling-Local Selective Kernel Attention （SPPF_LSKA） module was introduced to capture long-range dependencies effectively by combining local feature selection and global contextual modeling； the Adaptive Granular Fine-grained Channel Attention （AGFCA） module was incorporated to improve critical information perception ability through collaboration between spatial and channel attention. Experimental results on the KITTI dataset show that YOLOv10-LITE achieves a mean Average Precision （mAP） of 77.1%， which is 2.4% higher than that of YOLOv10， with the parameter count reduced by 8.7 percentage points. The above results verify the proposed model’s practicality in autonomous driving scenarios with both computational constraints and real-time demands.

Key words: autonomous driving, object detection, real-time detection, YOLOv10, lightweight model

摘要：

随着自动驾驶技术的发展，实时车辆检测在确保系统安全性和可靠性方面至关重要。因此，设计一种基于YOLOv10的轻量化检测模型——YOLOv10-LITE。所提模型通过引入4个结构改进模块，在保持检测精度的同时，有效降低模型的复杂度和推理延迟，适用于资源受限环境下的实时检测任务。具体而言，使用动态上采样（DySample）模块在降低计算开销的同时提升特征图的分辨率；使用快速多尺度网络（FastMSNet）模块增强多尺度特征提取能力，提高对不同尺寸目标的检测效果；使用空间金字塔池化-局部选择性大核注意力（SPPF_LSKA）模块结合局部特征选择与全局上下文建模，从而有效捕获长程依赖；使用自适应细粒度通道注意力（AGFCA）模块通过通道与空间注意力的协同作用，提升关键特征信息的感知能力。在KITTI数据集上的实验结果表明，YOLOv10-LITE的平均精度均值（mAP）达到了77.1%，相较于YOLOv10提升了2.4个百分点；同时，参数量减少了8.7%。以上结果验证了所提模型在计算受限且需满足实时性的自动驾驶场景中的实用性。

关键词: 自动驾驶, 目标检测, 实时检测, YOLOv10, 轻量化模型