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第十六届研电赛ARM命题基于eaidk-310的无人驾驶小车参赛思路

交通标志及红绿灯识别

在此基础上修改了部分代码

  • 图像分类算法使用的是Yolofastest,可根据需要训练自己的模型,此仓库提供了一个训练好的模型
  • 使用Tengine-Convert-Tools转化训练好的模型
  • 在eaidk-310上编译最新的Tengine 编译过程
  • 在eaidk-310上编译cpp文件(相应的路径根据实际情况修改)
$ g++  tm_yolofastest.cpp `pkg-config --libs --cflags opencv` -I /home/openailab/Tengine-Lite/build/install/include/tengine/ -L /home/openailab/Tengine-Lite/build/install/lib -l tengine-lite -o yolofastest
  • 摄像头链接310后运行程序
$ ./yolofastest -m ./yolo-fastest-1.1.tmfile 

语音控制

  • 首先运行device.py查看可用的麦克风,修改test.py中device_index为相应的编号
  • 运行test.py,(可能需要安装某些包)

eaidk-310's People

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