gplast / DroNet

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

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

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

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