【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YOLOv8的DMS（驾驶员监控系统）驾驶员抽烟、打电话、喝水、吃东西检测系统是一种利用先进计算机视觉技术的实时监测系统。该系统通过YOLOv8算法，一种在速度和准确性上均表现优异的实时目标检测算法，实现对驾驶员行为的实时监测。
该系统能够自动接收视频或图像输入，通过单次神经网络前向传播，即可快速准确地检测并识别出驾驶员是否在抽烟、打电话、喝水或吃东西等分心行为。这些检测不仅基于图像中的物体识别，还结合了人脸和手部的位置关系、嘴部区域特征等多维度信息，以提高检测的准确性。
为了训练这一系统，研究人员构建了包含大量标注图像的数据集，这些图像覆盖了各种驾驶环境下的分心行为实例。通过深度学习和优化，系统能够在复杂环境中稳定工作，为驾驶安全提供有力保障。
此外，该系统还具备实时预警功能，当检测到分心行为时，会立即通过声音、震动或视觉警告提醒驾驶员，有效降低因分心驾驶导致的事故风险。该系统在交通安全监控领域具有广泛应用前景，有望成为未来智能车辆不可或缺的一部分。
【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】
Sleepy
Cigarette
Drinking
Phone
microsleep
HandsOnWheel
Eating
HandsNotOnWheel
Seatbelt
【训练数据集】
[数据集][目标检测]DMS驾驶员抽烟打电话喝水吃东西检测数据集VOC+YOLO格式5743张9类别_喝水打电话抽烟数据集-CSDN博客
【训练信息】
| 参数 | 值 |
| 训练集图片数 | 5168 |
| 验证集图片数 | 575 |
| 训练map | 91.3% |
| 训练精度(Precision) | 90.8% |
| 训练召回率(Recall) | 84.8% |
验证集map信息
| Class | Images | Instances | P | R | mAP50 | mAP50-95 |
| all | 575 | 646 | 0.901 | 0.848 | 0.913 | 0.665 |
| Sleepy | 133 | 133 | 0.938 | 0.97 | 0.985 | 0.729 |
| Cigarette | 77 | 77 | 0.914 | 0.714 | 0.846 | 0.523 |
| Drinking | 164 | 164 | 0.809 | 0.799 | 0.818 | 0.519 |
| Phone | 10 | 10 | 0.75 | 0.5 | 0.75 | 0.513 |
| microsleep | 45 | 45 | 0.89 | 0.903 | 0.959 | 0.745 |
| HandsOnWheel | 24 | 24 | 1 | 0.951 | 0.979 | 0.74 |
| Eating | 54 | 54 | 0.98 | 0.963 | 0.993 | 0.776 |
| HandsNotOnWheel | 38 | 38 | 0.926 | 0.99 | 0.987 | 0.792 |
| Seatbelt | 100 | 101 | 0.9 | 0.842 | 0.898 | 0.649 |
【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ultralytics安装教程安装好yolov8环境，并根据官方export命令将自己pt模型转成onnx模型
（2）使用vs2019打开sln项目，选择x64 release并且修改一些必要的参数，比如输入shape等，点击运行即可查看最后效果
特别注意如果运行报错了，请参考我的博文进行重新引用我源码的DLL：[C#]opencvsharp报错System.Memory，Version=4.0.1.2，Culture=neutral，PublicKeyToken=cc7b13fcd2ddd51“版本高于所引_未能加载文件或程序集“system.memory, version=4.0.1.2, culture-CSDN博客
【提供文件】
C#源码
yolov8s.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】
https://download.csdn.net/download/FL1623863129/88540395
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