基于yolo模型的智能车距检测系统的设计与实现 毕业论文.docx

目 录

目

录 ··················································································· i

第 1 章 绪论·········································································1

1.1 使用 YOLOv5 进行车距检测的研究意义 ·························1

1.2 主要研究内容·······························································1

1.2.1 YOLOv5 算法的理解与优化 ·································1

1.2.2 数据集的准备与标注············································2

1.2.3 YOLOv5 模型的训练与调整 ·································2

1.3.2 实时性更强 ·························································3

1.3.3 处理复杂场景的能力更强 ·····································3

第 2 章 主要技术介绍 ···························································5

2.1 PyTorch·······································································5

2.2 YOLOv5······································································5

2.3 OpenCV ······································································6

2.4 NumPy ········································································7

2.5 Matplotlib····································································7

2.6 其他辅助库 ··································································7

第 3 章 具体项目实现 ···························································9

3.2.2 载入新图片 ······················································· 11

3.2.3 计算车距 ·························································· 12

3.2.4 可视化······························································ 13

3.3 数据优化 ··································································· 13

3.4 项目效果 ··································································· 14

3.5 项目比对 ··································································· 15

3.5.1 使用 NuScenes 数据集和普通数据集的比对 ·········· 15

3.5.2 NuScenes 和其他测距的比对······························· 16

3.5.3 YOLOv5 模型和 YOLOv8 模型的比对················· 17

第 4 章 实际应用场景 ························································· 19

基于 yolo 模型的智能车距检测系统的设计与实现

摘 要：

本文研究了基于 YOLOv5 目标识别模型的车辆距离检测技术。首先，深入探

讨了 YOLOv5 的基本原理，利用 PyTorch 深度学习框架实现了 YOLOv5 模型，之

后在公开数据集上进行了训练优化和测试。通过与 YOLOv8 模型的对比实验，评

估了 YOLOv5 在车辆距离检测任务中的准确度和速度表现。最后，通过实际场景

中的车辆检测结果，验证了 YOLOv5 模型在车辆距离检测中的适用性和效果。

关键词：YOLOv5; PyTorch; 目标识别模型

Design and Implementation of an Intelligent Vehicle Distance

framework. Subsequently, training optimization and testing were conducted on publicly

available datasets. The accuracy and speed performance of YOLOv5 in vehicle dis-

tance detection tasks were evaluated through comparative experiments with the YOLOv8

model. Finally, the applicability and effectiveness of the YOLOv5 model in vehicle dis-

tance detection were verified through vehicle detection results in actual scenarios.

Key words: YOLOv5; PyTorch; Target recognition model

用性研究。

在这样的大环境背景之下，开发了 YOLOv5 算法进行车距检测。YOLOv5 是

一种在当前的目标检测模型中较为实用和先进的实时目标检测算法，它能够在保

证检测的精度的同时，通过较高的检测速度呈现检测结果，非常适用于车距检测

这样的实时应用和需要实时检测、重视检测效率的场景。通过训练 YOLOv5 模型，

使其能够准确识别并定位出图像中的车辆，进而计算出车辆之间的距离。

整体上来说，这种车距检测项目旨在通过摄像头捕捉道路图像，利用 YOLOv5

算法对图像中的车辆进行实时检测，并且计算车辆之间的距离。通过这种处理方

式，我们可以为驾驶员提供车辆驾驶过程中实时的车距信息，帮助他们更好的判

断车辆之间的安全距离，防止发生追尾等交通事故。

1.2.1 YOLOv5 算法的理解与优化

YOLOv5 是一种基于深度学习的目标检测算法，其特点是在保持高精度的同

时实现较快的推理速度[11] 。

为了更好地适应车距检测相关实践任务，要对 YOLOv5 算法进行一定的优化，

比如调整网络结构[16] 、改进损失函数、优化训练策略等，以提升其在车距检测任