Implementing yolov4 target detection and tracking based on nao robot

Overview

基于nao机器人实现yolov4目标检测并进行跟踪

Introduction - 介绍

本项目为yolov4算法在nao机器人上的应用。

关于YOLOv4原理请参考YOLOv4原论文
本项目主要YOLOv4框架参考Bubbliiiing博主复现的代码
原博客链接:https://blog.csdn.net/weixin_44791964/article/details/106214657
复现代码链接:https://github.com/bubbliiiing/yolov4-pytorch
nao机器人单目测距方法请参考:https://wenku.baidu.com/view/bdc7eea7482fb4daa48d4b24.html
使用本项目前请先下载复现YOLOv4代码,并用py3.6文件夹中.py文件替换原文件中的同名文件


下图为目标跟踪流程图。由于nao机器人sdk库naoqi仅支持py2.7环境,本项目需分别运行py2.7环境下的"封装跟踪.py"文件和py3.6环境下的"predict.py"文件。
该项目可以让nao机器人转头寻找水瓶目标,检测到目标后通过单目测距向目标前进,当目标距离和nao小于1.09m时,程序完成运行。 image

Requirements - 必要条件

py2.7环境

numpy==1.16.6+vanilla
opencv-python==2.4.13.7
Pillow==6.2.2
pynaoqi==2.1.4.13

tips

naoqi库为软银官方提供的nao机器人sdk
naoqi库百度云链接:链接: https://pan.baidu.com/s/1kib-Bx9BjiOXCjrIycsIAw 提取码: 5k8b


py3.6环境

pytorch和cuda版本参考Bubbliiiing博文,其他缺少环境任意版本即可。 参考环境见py3.6环境文件(仅供参考,因为包含了很多自用无关的库)

Configuration - 配置

使用本项目前请先下载复现YOLOv4代码,并用py3.6文件夹中.py文件替换原文件中的同名文件
YOLOv4环境的配置方法:
1.将训练好的只检测水瓶类的权重文件放入model_data文件夹,并替换yolo.py中的初始路径
2.把model_data文件夹下的voc_classes.txt文件中物品类别改为只有bottle
3.更多问题详见Bubbliiiing博文。

本项目跟踪的只有水瓶类,所以训练时只提取了VOC2007数据集中的水瓶类别
只有水瓶类别的VOC2007数据集百度云链接:链接: https://pan.baidu.com/s/1d11f3lm2BvPtwxXuRYZ5HQ 提取码: w2kn
训练好的只检测水瓶类的权重百度云链接: 链接: https://pan.baidu.com/s/1Qt__j8RAOZeRbY8BjXitpA 提取码: 5u2b

Usage - 用法

配置好py3.6和py2.7环境后。先运行"封装跟踪.py"文件,再运行"predict.py"文件。
检测到的图片信息可见于img文件夹

Changelog - 更新日志

License - 版权信息

本项目证书为GPL-3.0 License,详见GPL-3.0 License.md

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