Real-Time Visual Intelligence on Low-Cost UAVs: A Modular Approach for Tracking, Scanning, and Navigation
- URL: http://arxiv.org/abs/2607.02298v1
- Date: Thu, 02 Jul 2026 15:15:56 GMT
- Title: Real-Time Visual Intelligence on Low-Cost UAVs: A Modular Approach for Tracking, Scanning, and Navigation
- Abstract summary: This paper presents the development of an integrated intelligent drone system designed to serve as a personal assistant.<n>We implement a modular architecture that integrates three core artificial intelligence functionalities: facial detection, facial recognition, and depth estimation from monocular vision.<n>Unlike existing commercial solutions, this system emphasizes accessibility, low-cost hardware, and open-source technologies.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autonomous drones are rapidly transforming modern warfare and civil applications alike. This paper presents the development of an integrated intelligent drone system designed to serve as a personal assistant. Leveraging the DJI Tello drone platform, we implemented a modular architecture that integrates three core artificial intelligence functionalities: facial detection, facial recognition, and depth estimation from monocular vision. A web-based interface enables seamless drone control and real-time video monitoring, while a Python-based server processes visual data and executes inference pipelines using lightweight neural models optimized for embedded systems. Unlike existing commercial solutions, this system emphasizes accessibility, low-cost hardware, and open-source technologies. The system demonstrates robust performance in real-world conditions, including person tracking, indoor scanning, and autonomous line following using virtual sensors. This project validates the applicability of advanced AI techniques in real-time robotic systems and illustrates the feasibility of deploying them on constrained hardware, providing a foundation for future research in autonomous UAVs for military, rescue, and surveillance missions.
Related papers
- Generative AI for Safe and Photorealistic Drone Light Shows [51.71264054256171]
Drone light shows are redefining aerial entertainment, yet their widespread adoption is bottlenecked by labor-intensive, manual animation.<n>We introduce SWAN, an end-to-end pipeline that synthesizes photorealistic, large-scale, and collision-free drone choreographies directly from text prompts.<n>We demonstrate scalability by safely orchestrating simulated 2,000-drone formations and validate physical feasibility on a dense real-world swarm of 49 quadcopters.
arXiv Detail & Related papers (2026-06-24T06:34:44Z) - AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios [64.51320327698231]
We introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios.<n>We develop an innovative semi-automated collaborative agent-based labeling assistant framework.<n>We also propose HawkEyeTrack, a novel method that collaboratively enhances vision-language representation learning.
arXiv Detail & Related papers (2025-11-26T04:44:27Z) - Explain and Monitor Deep Learning Models for Computer Vision using Obz AI [2.406359246841227]
Obz AI is a comprehensive software ecosystem designed to facilitate state-of-the-art explainability and observability for vision AI systems.<n>Obz AI provides a seamless integration pipeline, from a Python client library to a full-stack analytics dashboard.
arXiv Detail & Related papers (2025-08-25T16:46:21Z) - A Compendium of Autonomous Navigation using Object Detection and Tracking in Unmanned Aerial Vehicles [0.0]
Unmanned Aerial Vehicles (UAVs) are one of the most revolutionary inventions of 21st century.<n>This paper attempts to review the various approaches several authors have proposed for the purpose of autonomous navigation of UAVs.
arXiv Detail & Related papers (2025-05-31T09:13:43Z) - AI and Vision based Autonomous Navigation of Nano-Drones in Partially-Known Environments [8.595976385391896]
This work focuses on enabling the safe and autonomous flight of a pocket-size, 30-gram platform called Crazyflie 2.1.<n>We propose a novel AI-aided, vision-based reactive planning method for obstacle avoidance under the ambit of Integrated Sensing, Computing and Communication paradigm.<n>We show the ability to command the drone at $sim8$ frames-per-second and a model performance reaching a COCO mean-average-precision of $60.8$.
arXiv Detail & Related papers (2025-05-08T06:16:36Z) - Open-World Drone Active Tracking with Goal-Centered Rewards [62.21394499788672]
Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose DAT, the first open-world drone active air-to-ground tracking benchmark.<n>We also propose GC-VAT, which aims to improve the performance of drone tracking targets in complex scenarios.
arXiv Detail & Related papers (2024-12-01T09:37:46Z) - Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone
Racing [52.50284630866713]
Existing systems often require hand-engineered components for state estimation, planning, and control.
This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies.
arXiv Detail & Related papers (2022-10-26T19:03:17Z) - ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and
Response with AI [55.41644538483948]
Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response operations.
We have developed the free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS.
We demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide timely predictions of catastrophic flooding events.
arXiv Detail & Related papers (2022-01-25T14:51:19Z) - Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks.
Artificial intelligence (AI) is growing rapidly nowadays and has been very successful.
We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
arXiv Detail & Related papers (2020-09-24T07:11:31Z) - AutoSOS: Towards Multi-UAV Systems Supporting Maritime Search and Rescue
with Lightweight AI and Edge Computing [27.15999421608932]
This paper presents the research directions of the AutoSOS project, where we work in the development of an autonomous multi-robot search and rescue assistance platform.
The platform is meant to perform reconnaissance missions for initial assessment of the environment using novel adaptive deep learning algorithms.
When drones find potential objects, they will send their sensor data to the vessel to verity the findings with increased accuracy.
arXiv Detail & Related papers (2020-05-07T12:22:15Z) - An Efficient UAV-based Artificial Intelligence Framework for Real-Time
Visual Tasks [33.489573797811474]
We introduce a multi-layer AI (MLAI) framework to allow easy integration of ad-hoc visual-based AI applications.
To show its features and its advantages, we implemented and evaluated different modern visual-based deep learning models for object detection, target tracking and target handover.
arXiv Detail & Related papers (2020-04-13T18:53:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.