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This project generates images you've probably seen in autonomous driving demo. Object Detection, Lane Detection, Road Segmentation, Depth Estimation using TensorRT
| Date | Stars |
|---|---|
| 2026-07-24 | 408 |
| 2026-07-25 | 408 |
| 2026-07-28 | 408 |
| 2026-07-30 | 408 |
| 2026-07-31 | 408 |
| 2026-08-06 | 408 |
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https://user-images.githubusercontent.com/11009876/132947317-3c839522-b347-4a8d-8675-3999adf9cdb6.mp4
# Self-Driving-ish Computer Vision System
- This project generates images you've probably seen in autonomous driving demo
- Detection
- Object Detection and Tracking
- Lane Detection and Curve Fitting
- Road Segmentation
- Depth Estimation
- Transform using Projective Geometry and Pinhole Camera Model
- Normal View -> Top View
- Distance Calculation (image plane -> ground plane in world coordinate system)
## Result
- YoutTube: https://youtu.be/GB4p_fjQZNE
# Tested Environment
## Computer
- Windows 10 (x64) + Visual Studio 2019
- Intel Core i7-6700 @ 3.4GHz + NVIDIA GeForce GTX 1070
- Jetson Xavier NX. JetPack 4.6
- You will get error if using JetPack 4.5 or before because of TensorRT error
## Deep Learning Inference Framework
- TensorFlow Lite with XNNPACK delegate
- CPU
- Note: Running with CPU is very slow
- TensorRT
- GPU
# Usage
```
./main [input]
- input:
- use the default image file set in source code (main.cpp): blank
- ./main
- use video file: *.mp4, *.avi, *.webm
- ./main test.mp4
- use image file: *.jpg, *.png, *.bmp
- ./main test.jpg
- use camera: number (e.g. 0, 1, 2, ...)
- ./main 0
- use camera via gstreamer on Jetson: jetson
- ./main jetson
```
- Mouse Drag: Change top view angle
- Keyboard (asdwzx) : Change top view position
# How to build a project
## 0. Requirements
- OpenCV 4.x
- CMake
- TensorRT 8.0.x
- If you get build error related to TensorRT, modify cmake settings for it in `inference_helper/inference_helper/CMakeLists.txt`
## 1. Download source code and pre-built libraries
- Download source code
- If you use Windows, you can use Git Bash
```sh
git clone https://github.com/iwatake2222/self-driving-ish_computer_vision_system.git
cd self-driving-ish_computer_vision_system
git submodule update --init
sh inference_helper/third_party/download_prebuilt_libraries.sh
```
- Download models
```sh
sh ./download_resource.sh
```
## 2-a. Build in Windows (Visual Studio)
- Configure and Generate a new project using cmake-gui for Visual Studio 2019 64-bit
- `Where is the source code` : path-to-cloned-folder
- `Where to build the binaries` : path-to-build (any)
- Open `main.sln`
- Set `main` project as a startup project, then build and run!
- Note:
- You may need to modify cmake setting for TensorRT for your environment
## 2-b. Build in Linux (Jetson Xavier NX)
```sh
mkdir build && cd build
# cmake .. -DENABLE_TENSORRT=off
cmake .. -DENABLE_TENSORRT=on
make
./main
```
# Note
## cmake options
```sh
cmake .. -DENABLE_TENSORRT=off # Use TensorFlow Lite (default)
cmake .. -DENABLE_TENSORRT=on # Use TensorRT
cmake .. -DENABLE_SEGMENTATION=on # Enable Road Segmentation function (default)
cmake .. -DENABLE_SEGMENTATION=off # Disable Road Segmentation function
cmake .. -DENABLE_DEPTH=on # Enable Depth Estimation function (default)
cmake .. -DENABLE_DEPTH=off # Disable Depth Estimation function
```
## Misc
- It will take very long time when you execute the app for the first time, due to model conversion
- I took 80 minutes with RTX 3060ti
- I took 10 - 20 minutes with GTX 1070
# Software Design
## Class Diagram
## Data Flow Diagram
# Model Information
## Details
- Object Detection
- YOLOX-Nano, 480x640
- https://github.com/PINTO0309/PINTO_model_zoo/blob/main/132_YOLOX/download_nano_new.sh
- https://github.com/PINTO0309/PINTO_model_zoo/blob/main/132_YOLOX/download_nano.sh
- Lane Detection
- Ultra-Fast-Lane-Detection, 288x800
- https://github.com/PINTO0309/PINTO_model_zoo/blob/main/140_Ultra-Fast-Lane-Detection/download_culane.sh
- Road Segmentation
- road-segmentation-adas-0001, 512x8Excerpt of 7,182 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bbaf1bf41933b00d, topic:autonomous-driving, desc:autonomous driving, readme:autonomous driving
matched fp:bbaf1bf41933b00d, topic:tensorrt
matched fp:bbaf1bf41933b00d, topic:deep-learning, topic:tensorflow
matched fp:bbaf1bf41933b00d, name:computer vision, readme:computer vision, desc:object detection