https://github.com/lddl/road-anomaly-detection
Software for registering anomaly situations on roads
https://github.com/lddl/road-anomaly-detection
anomaly-detection computer-vision multi-object-tracking object-detection opencv road-accidents yolov7 yolov8
Last synced: about 2 months ago
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Software for registering anomaly situations on roads
- Host: GitHub
- URL: https://github.com/lddl/road-anomaly-detection
- Owner: LdDl
- License: mit
- Created: 2024-06-21T15:06:27.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-06-26T11:12:47.000Z (almost 2 years ago)
- Last Synced: 2025-01-21T18:49:14.090Z (over 1 year ago)
- Topics: anomaly-detection, computer-vision, multi-object-tracking, object-detection, opencv, road-accidents, yolov7, yolov8
- Language: Rust
- Homepage:
- Size: 882 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Citation: CITATION.cff
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README
# Yet another toy utility for registering anomaly situations on roads
In W.I.P. stage
## Table of Contents
- [Video showcase](#video-showcase)
- [About](#about)
- [How does it work?](#how-does-it-work?)
- [Installation and usage](#installation-and-usage)
- [Future works](#future-works)
- [References](#references)
- [Support](#support)
## Video showcase
@w.i.p.
## About
This project is aimed to register road traffic accidents, foreign object and other anomaly situations on roads. It is pretty simple and uses just YOLO (both [traditional](https://github.com/AlexeyAB/darknet) and [Ultralytics version](https://github.com/ultralytics/ultralytics) could be used). Some advanced techniques like 3D-CNN, LSTM's and others could be used, but I do not have that much time so PR's are very welcome.
I do have also utility for monitoring road traffic flow parameters [here](https://github.com/LdDl/rust-road-traffic)
## How does it work?
Diagram could speak more than a thousand words. I'm not digging into the Background Subtraction / Object Detection or MOT (Multi-object tracking) topic since it is not a main target of this project.
Diagram is prepared via https://app.diagrams.net/. Source is [here](docs/anomaly_detection.drawio).
## Screenshots
imshow() output:
Subscribe channel on the server:
Wait until event has come:
## Installation and usage
**Important notice**: it has been tested on Ubuntu 22.04.3 LTS only!
It is needed to compile it via Rust programming language compiler (here is docs: https://www.rust-lang.org/learn/get-started) currently. If further I'll make a Dockerfile for CPU atleast and may be for GPU/CUDA.
Compilation from source code:
```shell
git clone https://github.com/LdDl/road-anomaly-detection
cd road-anomaly-detection
cargo build --release
```
Prepare neural network for detecting anomaly events.
Prepare configuration file. Example could be found here - [data/conf.toml](data/conf.toml). In my example I use YOLOv8 trained on just two classes: "moderate_accident", "severe_accident".
Run:
```
export ROAD_ANOMALY_CONFIG=$(pwd)/data/conf.toml
./target/release/road-anomaly-detector $ROAD_ANOMALY_CONFIG
```
When events published into to the reciever server than you can expect following JSON structure:
```json
{
"id": "Event identifier represented as UUID v4",
"event_registered_at": UTC UnixTimestamp when event has been registered,
"event_image": "base64 representation of an image",
"object_id": "Detection identifier. Most of time would be represented as UUID v4",
"object_registered_at": UTC UnixTimestamp when detection has been registered,
"object_lifetime": Number of second while the detection was considered "relevant",
"object_bbox": {
"x": X-coordinate of the left top of the detection,
"y": Y-coordinate of the left top of the detection,
"width": Width of the corresponding detection bounding box,
"height": Height of the corresponding detection bounding box
},
"object_poi": {
"x": X-coordinate of the center of the detection bounding box,
"y": Y-coordinate of the center of the detection bounding box
},
"object_classname": "Label for the class",
"object_confidence": Confidence that detection is classified as corresponding class label,
"zone_id": "Unique identifier for zone of interests",
"equipment_id": "Optional application name (could be considered as equipment identifier for embedded devices)"
}
```
## Future works
* Make REST API to extract and to mutate configuration;
* Make MJPEG export;
* Make publishing to custom REST API via POST request;
* Prepare some pre-trained neural networks;
## References
* MOG2 - https://docs.opencv.org/4.x/d1/dc5/tutorial_background_subtraction.html
* MOT (Multi-object tracking) in Rust programming language - https://github.com/LdDl/mot-rs
* OpenCV's bindings - https://github.com/twistedfall/opencv-rust
* Object detection in Rust programming language via YOLO - https://github.com/LdDl/object-detection-opencv-rust
* YOLO v3 paper - https://arxiv.org/abs/1804.02767, Joseph Redmon, Ali Farhadi
* YOLO v4 paper - https://arxiv.org/abs/2004.10934, Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao
* YOLO v7 paper - https://arxiv.org/abs/2207.02696, Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao
* Original Darknet YOLO repository - https://github.com/pjreddie/darknet
* Most popular fork of Darknet YOLO - https://github.com/AlexeyAB/darknet
* Developers of YOLOv8 - https://github.com/ultralytics/ultralytics. If you are aware of some original papers for YOLOv8 architecture, please contact me to mention it in this README.
Video example has been taken from [here](https://www.youtube.com/watch?v=z60Y20kJSmc)
Please cite this repository if you are using it
## Support
If you have troubles or questions please [open an issue](https://github.com/LdDl/road-anomaly-detection/issues/new).