Real-time object detection with YOLOv5 and TensorRT

Overview

YOLOv5-TensorRT

The goal of this library is to provide an accessible and robust method for performing efficient, real-time inference with YOLOv5 using NVIDIA TensorRT. The library is extensively documented and comes with various guided examples.

This library was originally developed for VDL RobotSports, an industrial team based in the Netherlands participating in the RoboCup Middle Size League, and currently sees active use on the soccer robots.

Features

  • FP32 and FP16 inference
  • Batch inference
  • Support for varying input dimensions
  • ONNX support
  • CUDA-accelerated pre-processing
  • Integration with OpenCV (with optionally also the OpenCV-CUDA module)
  • Extensive documentation available on all classes, methods and functions

Platforms

  • Most modern linux distributions
  • NVIDIA L4T (Jetson platform)

Dependencies:

  • TensorRT >=8 (libnvinfer libnvonnxparsers-dev)
  • OpenCV

Tools / Examples

Various documented examples can be found in the directory.

In order to build a TensorRT engine based on an ONNX model, the following tool/example is available:

  • build_engine: build a TensorRT engine based on your ONNX model

For object detection, the following tools/examples are available:

Example Usage

To get started quickly, build the library (using the steps below) and use the build_engine and process_image tools as following

./build_engine --input INPUT_ONNX --output yolov5.engine
./process_image --engine yolov5.engine --input INPUT_IMAGE --output OUTPUT_IMAGE

where you replace "INPUT_ONNX" with the path to your ONNX model, set "INPUT_IMAGE" to your input image, and "OUTPUT_IMAGE" to the desired output

Code example

Build the TensorRT engine in just three lines of C++ code:

yolov5::Builder builder;
builder.init();
builder.build("yolov5.onnx", "yolov5.engine");

Next, efficiently detect objects using YoloV5 in six lines of C++ code:

yolov5::Detector detector;
detector.init();
detector.loadEngine("yolov5.engine");

cv::Mat image = cv::imread("image.png");

std::vector<yolov5::Detection> detections;
detector.detect(image, &detections);

Building the library

The software can be compiled using CMake and a modern C++ compiler (e.g. GCC) with support for C++14, using the following steps:

mkdir build
cd build
cmake ..
make

Citing

If you like this library and would like to cite it, please use the following:

@misc{yolov5tensorrt,
  author       = {van der Meer, Noah and van Hoof, Charel},
  title        = {{yolov5-tensorrt}: Real-time object detection with {YOLOv5} and {TensorRT}},
  howpublished = {GitHub},
  year         = {2021},
  note         = {\url{https://github.com/noahmr/yolov5-tensorrt}}
}

License

Copyright (c) 2021, Noah van der Meer

This software is licenced under the MIT license, see LICENCE.md.

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Comments
  • How to get bounding box coordinates and set maximum detection?

    How to get bounding box coordinates and set maximum detection?

    Hi, i'm currently building a project for detecting ball with real time object detection on jetson platform. The code i'm currently using is the process_live.py and it works perfectly fine but i need the bounding box coordinates and set the maximum detection to only detect 1 ball, can you help me with that? Thanks

    opened by beatmonster 4
  • FP16 Mode support

    FP16 Mode support

    Thank you for sharing this project. It's really helpful to me.

    I tested examples without any issue with engine fp32. However, I'm getting issue with engine fp16. I can run normally but there no detection in the result. The engine with fp16, I built from yolov5 github with --half option. I think it comes from
    DeviceMemory::setup() and CvCpuPreprocessor::process() because input should have type of 2 bytes, not 4 bytes

    Updated: I updated the case when I succeeded and failed to get correct result. onnx is generated by yolov5-hub (fp16 generated by using --half option):

    • onnx fp32 -> TensorRT fp32 (generated by yolov5-hub | this hub | nvidia container): success
    • onnx (fp32) -> tensorrt fp16 (generated by this hub | nvidia container): success
    • tensorrt fp16 (generated by yolov5-hub with --half option): failed
    • onnx fp16 -> tensorrt fp16 (generated by this hub | nvidia container): failed

    I think the issue for mode fp16 related to onnx-fp16. Please consider this issue when you have time. Thanks

    opened by PhuongNDVN 0
  • When I'm using fp16 and yolov5s modle on jetson xavier,  tensorrt throws a runtime error.

    When I'm using fp16 and yolov5s modle on jetson xavier, tensorrt throws a runtime error.

    I use the c++ version build_engine to create tensorrt modle.

    ./yolov5-tensorrt/build/build_engine --model ./models/yolov5s.onnx --output ./models/yolov5s.engine --precision fp16
    

    Then I run inference. However, this error occured. [pluginV2Runner.cpp::load::292] Error Code 1: Serialization (Serialization assertion creator failed.Cannot deserialize plugin since corresponding IPluginCreator not found in Plugin Registry)

    opened by EternalSaga 2
  • Cuda undefined reference

    Cuda undefined reference

    I am trying to use the shared library in my project, in AGX Xavier platform. While compiling I am getting undefined referene error for Cuda as in the image below. Can you please help?

    image

    bug 
    opened by skp1204 2
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Noah van der Meer
Noah van der Meer
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