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Conventional SGBM depth ranging + yolov5 object detection with deployment on Jeston nano

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dafafa.drawio (5) (1)

项目日志

  • 双目相机的标定和初始化(2022.7.3)
  • 运行BM、SGBM算法(2022.7.6)
  • 研究SGBM算法并得出良好的open3d模型(2022.7.15)
  • 实现双目测距(2022.7.27)
  • 双目相机测出Yolov5检测物体的距离(2022.7.29)
  • 视频帧率提高至6FPS(2022.7.30)
  • 使用C++重勾BM算法(2022.8.1)
  • 使用C++重构SGBM算法(2022.8.1)
  • 使用TensorRT、C++部署yolov5模型(2022.8.3)
  • 完成项目,帧率至少达到20FPS(2022.8.3)
  • 新增Jeston nano部署文件

环境说明

  • 🔥Tensorrt 8.4
  • 🚀Cuda 11.6.1 Cudnn 8.4.1
  • Opencv 4.5.1
  • Cmake 3.23.3
  • Visual Studio 2017
  • MX350,Windows10

文件说明

  • 💼BM、SGBM算法均有C++和Python两个版本

  • 📂tensorrt:模型部署文件,帧率为23fps

  • 📁yolov5-v6.1-pytorch-master:未部署前的python代码文件,帧率为5fps

  • stereo_introduce:双目摄像头基本资料

  • 📒双目视觉资料:从双目相机的标定(Matlab)到sgbm生成深度图的图文教程

  • stereo_shot.py:摄像头拍摄代码

  • 🎁Jeston nano_tensorrt:Jeston nano(Linux)部署资料

怎么用?

SGBM算法应用(Python版):https://www.bilibili.com/video/BV1zT411w7oZ

在YOLOv5中加入双目测距,实现目标测距:https://www.bilibili.com/video/BV1qG41147ZW

Jeston nano部署yolov5,并实现双目测距:https://www.bilibili.com/video/BV15g411Q7ZV

参考资料

  1. 🍔YOLOv5 Tensorrt Python/C++部署:https://www.bilibili.com/video/BV113411J7nk/?spm_id_from=333.788.recommend_more_video.-1&vd_source=97aec9e652524c83bb4f4b9481ee059e
  2. 🍞Pytorch 搭建自己的YoloV5目标检测平台Bubbliiiing:https://www.bilibili.com/video/BV1FZ4y1m777?spm_id_from=333.999.0.0
  3. 🍟双目摄像头-立体视觉:https://blog.csdn.net/qq_41204464/category_10766478.html?spm=1001.2014.3001.5482)
  4. CUDA的正确安装/升级/重装/使用方式:https://zhuanlan.zhihu.com/p/520536351
  5. 报错【Could not locate zlibwapi.dll. Please make sure it is in your library path】:https://blog.csdn.net/qq_44224801/article/details/125525721
  6. 🍿windows下 C++ openCV配置及x86编译(傻瓜式教程):https://blog.csdn.net/qq_37059136/article/details/124165080
  7. 树莓派安装pytorch:https://blog.csdn.net/weixin_53798505/article/details/125235377
  8. 树莓派开机自启动:https://blog.csdn.net/TohkaQAQ/article/details/121056564

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