Object Detection project 🚀 is the capstone project on object recognition through images and videos, inspired by the Airborne Object Tracking Challenge
About the Challenge
One of the important challenges of autonomous flight is the Sense and Avoid (SAA) task to maintain enough separation from obstacles. While the route of an autonomous drone might be carefully planned ahead of its mission, and the airspace is relatively sparse, there is still a chance that the drone will encounter unforeseen airborne objects or static obstacles during its autonomous flight.
The autonomous SAA module has to take on the tasks of situational awareness, decision making, and flying the aircraft, while performing an evasive maneuver.
There are several alternatives for onboard sensing including radar, LIDAR, passive electro-optical sensors, and passive acoustic sensors. Solving the SAA task with visual cameras is attractive because cameras have relatively low weight and low cost.
For the purpose of this project, I consider a solution that solely relies on a single visual camera and Computer Vision technique that analyzes a monocular video.
Flying airborne objects pose unique challenges compared to static obstacles. In addition to the typical small size, it is not sufficient to merely detect and localize those objects in the scene, because prediction of the future motion is essential to correctly estimate if the encounter requires a collision avoidance maneuver and create a safer route. Such prediction will typically rely on analysis of the motion over a period of time, and therefore requires association of the detected objects across the video frames.
As a preliminary stage for determining if a collision avoidance maneuver is necessary, this challenge will be concerned with spatio - temporal airborne object detection and tracking, given a new Airborne Object Tracking dataset, and perform two benchmarks:
- Airborne detection and tracking
- Frame-level airborne detection
Quick Start Examples
Install
Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:
$ git clone https://github.com/VuongTuanKhanh/Airborne-Object-Detection-and-Tracking
$ cd Funix-Capstone-Project
$ pip install -r requirements.txtTutorials
- About the AOT dataset 🚀 RECOMMENDED
- Downloading the Dataset ☘️ RECOMMENDED
- Exploring the Dataset ⭐ RECOMMENDED
- Evaluation Benchmarks
- Dataset EDA and Download 🚀 RECOMMENDED
- Baseline with Pre-train YOLOv3 ☘️ RECOMMENDED
- Tutorial on how to train YOLOv3 with AOT dataset ⭐ THEORY
- Airborne Object Detection Benchmark 🚀 MODEL
- Local Logging Visualization ☘️ VISUALIZE
- WanDB Live Visualization 🚀 VISUALIZE
- End-to-end YOLOv5 with AOT Dataset 🚀 HIGHLY RECOMMENDED
Deployments
- Docker image with FLASK 🚀 DEPLOYMENT
- Heroku App
Basic Actions
Environments and Integrations
Contribute
We love your input! We want to make contributing to this project as easy and transparent as possible. Please see our Contributing Guide to get started.
Special Thank
We would like to give our biggest thanks to AICrowd for the great challenge as well as the incredible guidance along the way.
Contact: AICrowd
Contact
For issues running this project please visit GitHub Issues. For business or professional support requests please visit https://www.facebook.com/vuongtuankhanh99.
License
By contributing, you agree that your contributions will be licensed under the MIT license