【重要说明】
该系统以opencvsharp作图像处理,onnxruntime做推理引擎,使用CPU进行推理,适合有显卡或者没有显卡windows x64系统均可,不支持macOS和Linux系统,不支持x86的windows操作系统。由于采用CPU推理,要比GPU慢。为了适合大部分操作系统我们暂时只写了CPU推理源码,GPU推理源码后期根据需要可能会调整,目前只考虑CPU推理,主要是为了照顾现在大部分使用该源码是学生,很多人并没有显卡的电脑情况。
【算法介绍】
基于YOLOv5的驾驶员抽烟、打电话、安全带检测系统是一种先进的驾驶行为监测系统,旨在提高驾驶安全性。该系统利用YOLOv5算法,这是一种基于深度学习的目标检测算法,特别适用于实时目标检测任务。
在驾驶员抽烟、打电话、安全带检测系统中,YOLOv5算法通过将图像分割成网格并对每个网格进行分类,同时回归框的边界框参数,从而在单个前向传递中实现目标检测。为了训练这一系统,需要构建一个包含大量标注图像的数据集,这些图像应覆盖各种驾驶环境下,司机抽烟、打电话以及未系安全带的实例。
在实际应用中,该系统可以通过预置的摄像头或监控系统来实时获取图像或视频流,对输入图像进行处理和分析,通过YOLOv5模型检测驾驶员的行为,并判断是否存在抽烟、打电话或未系安全带等分心或违规行为。如果检测到这些行为,系统可以触发警报、发送通知或采取其他适当的措施,以提醒驾驶员纠正分心行为或违规行为,从而降低事故风险。
此外,该系统还需要考虑隐私保护和合规性相关的问题,确保系统的合法性和有效性。通过不断优化算法性能、扩大高质量数据集规模以及在实际应用中平衡技术与法律伦理考量,该系统将在减少交通事故、保障驾驶安全方面发挥重要作用。
【效果展示】
【测试环境】
windows10 x64系统
VS2019
netframework4.7.2
opencvsharp4.8.0
onnxruntime1.16.3
【模型可以检测出类别】
{0: 'cigarette', 1: 'phone', 2: 'seatbelt'}
【相关数据集(非本文训练的数据集)】
https://download.csdn.net/download/FL1623863129/89319046
【训练信息】
参数 | 值 |
训练集图片数 | 11932 |
验证集图片数 | 2393 |
训练map | 73.8% |
训练精度(Precision) | 82.2% |
训练召回率(Recall) | 69.8% |
验证集每个类别精度统计
类别 | MAP0.5(单位:%) |
all | 73 |
cigarette | 60 |
phone | 72 |
seatbelt | 87 |
【部分实现源码】
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;
using System.Windows.Forms;
namespace FIRC
{
public partial class Form1 : Form
{
public bool videoStart = false;//视频停止标志
string weightsPath = Application.StartupPath + "\\weights";//模型目录
string labelTxt= Application.StartupPath + "\\weights\\class_names.txt";//类别文件
Yolov8Manager detetor = new Yolov8Manager();//推理引擎
public Form1()
{
InitializeComponent();
CheckForIllegalCrossThreadCalls = false;//线程更新控件不报错
}
private void LoadWeightsFromDir()
{
var di = new DirectoryInfo(weightsPath);
foreach(var fi in di.GetFiles("*.onnx"))
{
comboBox1.Items.Add(fi.Name);
}
if(comboBox1.Items.Count>0)
{
comboBox1.SelectedIndex = 0;
}
else
{
tssl_show.Text = "未找到模型,请关闭程序,放入模型到weights文件夹!";
tsb_pic.Enabled = false;
tsb_video.Enabled = false;
tsb_camera.Enabled = false;
}
}
private void Form1_Load(object sender, EventArgs e)
{
LoadWeightsFromDir();//从目录加载模型
}
public string GetResultString(Result result)
{
Dictionary<string, int> resultDict = new Dictionary<string, int>();
for (int i = 0; i < result.length; i++)
{
if(resultDict.ContainsKey( result.classes[i]) )
{
resultDict[result.classes[i]]++;
}
else
{
resultDict[result.classes[i]]=1;
}
}
var resultStr = "";
foreach(var item in resultDict)
{
resultStr += string.Format("{0}:{1}\n",item.Key,item.Value);
}
return resultStr;
}
private void tsb_pic_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
if (ofd.ShowDialog() != DialogResult.OK) return;
tssl_show.Text = "正在检测中...";
Task.Run(() => {
var sw = new Stopwatch();
sw.Start();
Mat image = Cv2.ImRead(ofd.FileName);
detetor.Confidence =Convert.ToSingle(numericUpDown1.Value);
detetor.IOU = Convert.ToSingle(numericUpDown2.Value);
var results=detetor.Inference(image);
var resultImage = detetor.DrawImage(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(image), results);
sw.Stop();
pb_show.Image = resultImage;
tb_res.Text = GetResultString(results);
tssl_show.Text = "检测已完成!总计耗时"+sw.Elapsed.TotalSeconds+"秒";
});
}
public void VideoProcess(string videoPath)
{
Task.Run(() => {
detetor.Confidence = Convert.ToSingle(numericUpDown1.Value);
detetor.IOU = Convert.ToSingle(numericUpDown2.Value);
VideoCapture capture = new VideoCapture(videoPath);
if (!capture.IsOpened())
{
tssl_show.Text="视频打开失败!";
return;
}
Mat frame = new Mat();
var sw = new Stopwatch();
int fps = 0;
while (videoStart)
{
capture.Read(frame);
if (frame.Empty())
{
Console.WriteLine("data is empty!");
break;
}
sw.Start();
var results = detetor.Inference(frame);
var resultImg = detetor.DrawImage(frame,results);
sw.Stop();
fps = Convert.ToInt32(1 / sw.Elapsed.TotalSeconds);
sw.Reset();
Cv2.PutText(resultImg, "FPS=" + fps, new OpenCvSharp.Point(30, 30), HersheyFonts.HersheyComplex, 1.0, new Scalar(255, 0, 0), 3);
//显示结果
pb_show.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(resultImg);
tb_res.Text = GetResultString(results);
Thread.Sleep(5);
}
capture.Release();
pb_show.Image = null;
tssl_show.Text = "视频已停止!";
tsb_video.Text = "选择视频";
});
}
public void CameraProcess(int cameraIndex=0)
{
Task.Run(() => {
detetor.Confidence = Convert.ToSingle(numericUpDown1.Value);
detetor.IOU = Convert.ToSingle(numericUpDown2.Value);
VideoCapture capture = new VideoCapture(cameraIndex);
if (!capture.IsOpened())
{
tssl_show.Text = "摄像头打开失败!";
return;
}
Mat frame = new Mat();
var sw = new Stopwatch();
int fps = 0;
while (videoStart)
{
capture.Read(frame);
if (frame.Empty())
{
Console.WriteLine("data is empty!");
break;
}
sw.Start();
var results = detetor.Inference(frame);
var resultImg = detetor.DrawImage(frame, results);
sw.Stop();
fps = Convert.ToInt32(1 / sw.Elapsed.TotalSeconds);
sw.Reset();
Cv2.PutText(resultImg, "FPS=" + fps, new OpenCvSharp.Point(30, 30), HersheyFonts.HersheyComplex, 1.0, new Scalar(255, 0, 0), 3);
//显示结果
pb_show.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(resultImg);
tb_res.Text = GetResultString(results);
Thread.Sleep(5);
}
capture.Release();
pb_show.Image = null;
tssl_show.Text = "摄像头已停止!";
tsb_camera.Text = "打开摄像头";
});
}
private void tsb_video_Click(object sender, EventArgs e)
{
if(tsb_video.Text=="选择视频")
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = "视频文件(*.*)|*.mp4;*.avi";
if (ofd.ShowDialog() != DialogResult.OK) return;
videoStart = true;
VideoProcess(ofd.FileName);
tsb_video.Text = "停止";
tssl_show.Text = "视频正在检测中...";
}
else
{
videoStart = false;
}
}
private void tsb_camera_Click(object sender, EventArgs e)
{
if (tsb_camera.Text == "打开摄像头")
{
videoStart = true;
CameraProcess(0);
tsb_camera.Text = "停止";
tssl_show.Text = "摄像头正在检测中...";
}
else
{
videoStart = false;
}
}
private void tsb_exit_Click(object sender, EventArgs e)
{
videoStart = false;
this.Close();
}
private void trackBar1_Scroll(object sender, EventArgs e)
{
numericUpDown1.Value = Convert.ToDecimal(trackBar1.Value / 100.0f);
}
private void trackBar2_Scroll(object sender, EventArgs e)
{
numericUpDown2.Value = Convert.ToDecimal(trackBar2.Value / 100.0f);
}
private void numericUpDown1_ValueChanged(object sender, EventArgs e)
{
trackBar1.Value = (int)(Convert.ToSingle(numericUpDown1.Value) * 100);
}
private void numericUpDown2_ValueChanged(object sender, EventArgs e)
{
trackBar2.Value = (int)(Convert.ToSingle(numericUpDown2.Value) * 100);
}
private void comboBox1_SelectedIndexChanged(object sender, EventArgs e)
{
tssl_show.Text="加载模型:"+comboBox1.Text;
detetor.LoadWeights(weightsPath+"\\"+comboBox1.Text,labelTxt);
tssl_show.Text = "模型加载已完成!";
}
}
}
【使用步骤】
使用步骤:
(1)首先根据官方框架yolov5安装教程安装好yolov5环境,并安装好pyqt5
(2)切换到自己安装的yolov5环境后,并切换到源码目录,执行python main.py即可运行启动界面,进行相应的操作即可
【提供文件】
python源码
yolov5n.onnx模型(不提供pytorch模型)
训练的map,P,R曲线图(在weights\results.png)
测试图片(在test_img文件夹下面)
【源码下载地址】