【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】

验证集每个类别精度统计

【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】

https://download.csdn.net/download/FL1623863129/88540396