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All Projects → gplast → DroNet

gplast / DroNet

Licence: other
DroNet: Efficient convolutional neural network detector for Real-Time UAV applications

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c
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Cuda
1817 projects

Labels

opencvapplicationreal-timeuavgpuefficientdetectoredgeyoloconvolutional-neural-networksdarknetdronet

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DroNet: Efficient Convolutional Neural Network Detector for Real-Time UAV Applications

Implementation of the CNN Car Detector proposed on DroNet paper using Darknet Framework.

DroNetV3 - Crossroad

Dependencies

OpenCV

CUDA(Optional)

Build

In order to build DarkNet library and use our DroNet CNN you need to build OpenCV using these Steps.

nano Makefile # run this with sudo if you have permission error

To build DroNet on Mac OS please comment out line 22 and uncomment line 23 and update the path of GCC

  • GPU=0 - enable/disable GPU (To use GPU modify line 50 and 52 with include and lib64 CUDA path)
  • CUDNN=0 - enable/disable CUDNN
  • OPENCV=1 - enable/disable OpenCV
  • OPENMP=1 - enable/disable Multi-Processing on CPU
  • DEBUG=0 - enable/disable Debug mode (Never used)
make -j

DroNetV1 - Works better with low altitudes

./darknet detector demo car.data cfg/DroNet_car.cfg results/DroNet_car.weights Car_Parking.mov -thresh 0.4
./darknet detector demo car.data cfg/DroNet_car.cfg results/DroNet_car.weights Car_Road.MOV -thresh 0.4

DroNetV3 - Works better with higher altitudes (Recommended .cfg input 1024 x 1024)

./darknet detector demo car_ped.data cfg/DroNetV3_car.cfg results/DroNetV3_car.weights Car_Crossroad.mp4
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