The development of autonomous navigation technology for Unmanned Aerial Vehicles (UAVs) is critical in modern warfare. The ability for a UAV to detect, lock-on, and follow the movements of ground-based military tanks and vehicles in real-time is a game-changer. By integrating a high-definition camera and advanced companion computer systems, UAVs can be transformed into formidable reconnaissance and tracking assets on the battlefield. This technology represents a quantum leap in battlefield awareness and situational awareness, providing military commanders with real-time, actionable intelligence in a rapidly changing environment.
-Utilizing advanced image processing techniques, our autonomous UAV will detect, lock-on, and track ground-based military tanks and vehicles in real-time.
-The system comprises of a PixHawk 4 flight controller, a Raspberry Pi 4 companion computer, and a high-definition camera.
-The Raspberry Pi camera module V2 streams video data to the companion computer via a USB connection, and the Ground Station receives the video stream via radio waves.
-A comprehensive pre-flight check, conducted through Mission Planner, ensures system functionality and GPS readiness.
-On the Ground Station, a Python script combines YOLOv4 object detection and SORT tracking algorithms to accurately identify and follow the target.
-The Ground Station relays the target's coordinates to the companion computer, which forwards it to the PixHawk flight controller, directing the UAV towards the target.
-The PixHawk PX4 flight controller commands the UAV to maintain a dynamic pursuit of the target, providing real-time situational awareness.
-We will be utilizing the power of DroneKit, a Python API that leverages the MAVLink protocol to facilitate seamless communication between the flight controller (PixHawk PX4) and the onboard computer (Raspberry Pi or Jetson).
-The onboard computer, equipped with the Raspberry Pi camera module, streams live video footage of the surroundings to the ground station.
-The ground station, running a high-performance machine, employs cutting-edge computer vision techniques such as YOLOv7 and SORT to detect and track military tanks in real-time.
-The latitude and longitude of the detected tanks are then transmitted via telemetry to the onboard computer using the DroneKit API.
-The onboard computer relays the information to the PixHawk PX4 flight controller, which seamlessly navigates the drone towards the target and follows its movements.
-To further boost the accuracy and performance, we have utilized transfer learning on YOLOv4 to fine-tune the model on our custom dataset.
-During inference, we have employed parallel computing using multiple GPUs to speed up the processing time.
-The SORT algorithm is utilized for real-time multiple object tracking to follow the detected tanks as they move.
-The system is designed to be modular and scalable, allowing for easy integration of new sensors and algorithms to further enhance its capabilities.
-To ensure optimal accuracy and precision, we have leveraged cutting-edge deep learning algorithms, such as YOLOv4, to perform real-time detection and tracking of military tanks.
-The SORT algorithm, combined with the powerful geospatial calculations provided by Geopy, allows us to determine the exact location of the tanks, in real-time.
-The DroneKit API, provides a seamless and secure communication between the onboard computer and ground station, allowing for efficient and reliable transmission of tank location data to the drone.
-The system has been designed to be fault-tolerant and is equipped with a return-to-launch feature, ensuring that the drone can safely return to its starting point in the event of any malfunctions or errors.
-By utilizing these advanced technologies, our solution delivers unparalleled accuracy, precision, and reliability for autonomous navigation and tracking of military tanks.
-The autonomous navigation system is designed to effectively detect and track moving targets, whether they be enemy military vehicles or wildlife in their natural habitats.
-Utilizing advanced computer vision techniques, such as YOLOv7 object detection and SORT tracking, this system accurately locates and follows the target in real-time.
-With the ability to return to its launch point in case of any operational failures, this system is a reliable tool for military reconnaissance and wildlife monitoring missions.
-By incorporating DroneKit API and integrating with high-performance onboard computing solutions, this system ensures seamless and efficient communication between ground and air components.
We have been able to:
-Develop a cutting-edge solution for real-time detection and tracking of military tanks and vehicles, as well as wildlife, using state-of-the-art deep learning algorithms and computer vision techniques.
-Implement a robust system architecture with a high-performance onboard computer and flight controller, utilizing DroneKit and MAVLink to ensure seamless communication between the ground station and the drone.
-Achieve remarkable accuracy and reliability in detecting and tracking targets, leveraging a custom-trained YOLOv4 model and the powerful SORT algorithm, allowing for seamless and efficient operations in various scenarios.
-Provide a flexible and scalable platform, capable of adapting to new requirements and improving performance over time, thanks to the use of cutting-edge technologies and techniques.
The Military Tank Detection and Tracking through UAV project demonstrates the use of advanced computer vision techniques for real-time object detection and tracking. The system can be used for various military surveillance applications.
Feel free to contribute to this project by submitting pull requests or reporting issues.
We would like to acknowledge the following resources for their contributions to this project:
- YOLOv4: https://github.com/AlexeyAB/darknet
- DeepSort: https://github.com/nwojke/deep_sort
- DroneKit: https://dronekit.io/