This project builds upon my previous work, YOLOv5 Inference in Python & C++. It explores advanced letterboxing techniques in C++ and details the implementation of CUDA programming to accelerate the letterboxing preprocessing. The CUDA-accelerated version of letterboxing achieves a significant speed improvement, approximately 5x-6x faster (CPU timing is around 3ms, while GPU with 1000 cores operates around 0.5ms), while maintaining the same object detection accuracy as the enhanced C++ letterboxing code without CUDA.
While the previous project focused mainly on evaluating C++ and Python equivalence, this project aims to further explore the potential of C++ in conjunction with CUDA programming for preprocessing.
The environment preparation is the same as the previous guidance provided in YOLOv5 Inference in Python & C++. Please refer to that repository for detailed setup instructions.
Within each subproject, like yolov5_x_xx
// delete original build folder first
mkdir build
cmake -S . -B build
cmake --build build/
./build/main
original letterbox is simply placing the original image in the top-left corner of a black square.
improved letterbox use affine transformation for precise image scaling and positioning, and put it in the center. It has greater adaptability to various input size.
Improved Result: Do not have duplicate detetion for the car, not detect median barrier as person
Main process (1) write CUDA function to perform affine warp transformation on images, use Thread/Block to accelerate, convert interleaved RGB picture to consecutive list on memory (2) pass the memory between device and host
The code in main function (record time, prepare memory, call cuda process function)
Uncompleted code about cuda process function. In that case, set threads=1000, you can change this based on your GPU. (if result picture is a black image, always because this value setting)
