yolo26已经正式发布了，因此使用C#代码实现YOLO26-obb旋转框检测的onnx模型部署，首先看yolo11n-obb网络结构，发现输出shape是1x20x21504

再来看看yolo26n-obb网络结构输出，输出shape是1x300x7

可见yolo11和yolo26输出是不一样的是不能共用代码。

模型使用官方yolo26n-obb.pt转换成的onnx，转换命令 yolo ​​exportmodel=yolo26n-obb.pt​​ format=onnx opset=12 如果你是自己训练的模型可以替换即可，但是需要yolo26-obb框架才行 测试环境： vs2019 CPU推理，无需安装cuda+cudnn onnxruntime1.22.1 opecvsharp4.11.0 .net framework4.8.0 ultralytics==8.4.0

实现界面代码和调用代码：

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Diagnostics;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using OpenCvSharp;
 
namespace FIRC
{
    public partial class Form1 : Form
    {
        OpenCvSharp.Mat src = new OpenCvSharp.Mat();
        Yolo26ObbManager ym = new Yolo26ObbManager();
        public Form1()
        {
            InitializeComponent();
        }
 
        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog openFileDialog = new OpenFileDialog();
            openFileDialog.Filter = "图文件(*.*)|*.jpg;*.png;*.jpeg;*.bmp";
            openFileDialog.RestoreDirectory = true;
            openFileDialog.Multiselect = false;
            if (openFileDialog.ShowDialog() == DialogResult.OK)
            {
              
                src = Cv2.ImRead(openFileDialog.FileName);
                pictureBox1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(src);
 
 
            }
 
 
        }
 
        private void button2_Click(object sender, EventArgs e)
        {
            if(pictureBox1.Image==null)
            {
                return;
            }
            Stopwatch sw = new Stopwatch();
            sw.Start();
            var result = ym.Inference(src);
            sw.Stop();
            this.Text = "耗时" + sw.Elapsed.TotalSeconds + "秒";
            var resultMat = ym.DrawImage(src,result);
            pictureBox2.Image= OpenCvSharp.Extensions.BitmapConverter.ToBitmap(resultMat); //Mat转Bitmap
        }
 
        private void Form1_Load(object sender, EventArgs e)
        {
            ym.LoadWeights(Application.StartupPath+ "\\weights\\yolo26n-obb.onnx");
 
        }
 
        private void btn_video_Click(object sender, EventArgs e)
        {
            var detector = new Yolo26ObbManager();
            detector.LoadWeights(Application.StartupPath + "\\weights\\yolo26n-obb.onnx");
            VideoCapture capture = new VideoCapture(0);
            if (!capture.IsOpened())
            {
                Console.WriteLine("video not open!");
                return;
            }
            Mat frame = new Mat();
            var sw = new Stopwatch();
            int fps = 0;
            while (true)
            {
 
                capture.Read(frame);
                if (frame.Empty())
                {
                    Console.WriteLine("data is empty!");
                    break;
                }
                sw.Start();
                var result = detector.Inference(frame);
                var resultImg = detector.DrawImage(frame,result);
                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
                Cv2.ImShow("Result", resultImg);
                int key = Cv2.WaitKey(10);
                if (key == 27)
                    break;
            }
 
            capture.Release();
  
        }
    }
}

最后测试效果：