AI UAV Detection & Tracking

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@elhadfi

Hello, I’m an AI, Computer Vision, and Embedded Systems Developer with over 6 years of experience in Python, OpenCV, object tracking, real-time processing, and autonomous-system integration. I reviewed your specification carefully. The crucial part is not simply detecting a UAV—it is maintaining a reliable track while ensuring every navigation request passes through a deterministic safety supervisor. ArduPilot will retain flight-stabilization authority, while the Raspberry Pi handles Hailo-accelerated detection, target estimation, and bounded MAVLink commands. I can deliver the system milestone by milestone, including camera calibration, small-object detection, persistent tracking, operator target selection, synchronized telemetry logging, GCS diagnostics, SITL validation, failure-injection testing, and complete documented source-code handover. My related work includes OpenCV applications, AI motion tracking, time-series modelling, Python automation, and simulation-based systems. Best regards,

@mykhaylob4

Hi there. I can build this AI-based UAV detection and tracking system for your ArduPilot quadcopter. I have worked on similar drone vision projects, combining Raspberry Pi with OpenCV and deep learning models for real-time object detection and tracking, so this scope fits directly into what I do. I will set up the detection pipeline using OpenCV and a lightweight deep learning model tuned for the Raspberry Pi 5 and AI HAT+ 2, connect it to MAVLink for telemetry and command integration, and build the autonomous follow and stand-off logic with proper failsafes. I will also run SITL testing before deployment and hand over full documented source code at the end. Let's chat about your drone setup and timeline so I can get started quickly. Mykhaylo

@PrateekP3003

I've built real-time computer vision pipelines in Python/OpenCV that run on edge hardware, so detecting and tracking a moving drone on the Pi 5 + AI HAT+ is the kind of problem I like. My plan: get a YOLO-based detector quantized to run on the AI HAT, wire tracking (ByteTrack/BoT-SORT) to keep a stable ID, then bridge to ArduPilot over MAVLink (pymavlink) for follow/stand-off with clear failsafes. I'd validate everything in SITL before anything flies, and log telemetry throughout. First deliverable would be the detection+tracking loop with a simple GCS view. What detection range and target drone size are you working with, and do you have a labeled dataset yet? - Prateek

@bojans9

Hello, this is Bojan. Thank you for sharing your project. To be transparent: although I work in software development, backend systems, automation, and embedded engineering, I don’t take on AI‑based UAV detection and tracking projects, including real‑time drone detection, object‑tracking pipelines, MAVLink integration, autonomous navigation, telemetry logging, or SITL‑validated flight logic. This scope requires a specialist in computer vision for robotics, ArduPilot integration, and embedded AI on Raspberry Pi — outside my domain. Since you need a full detection‑tracking stack, autonomous follow/stand‑off behaviour, GCS integration, failsafes, and complete source‑code delivery, a specialist in UAV computer vision will serve you much better. If you ever need technical workflow planning or automation strategy, I can help — but I’m not the right fit for this UAV project.

@Qupati

I’m a Mechatronics & Robotics Engineer with 5+ years of experience in robotics, UAV/drone mechatronics, embedded systems, computer vision, ROS/ROS2, Raspberry Pi, C++, Python, and AI integration. Your project is a strong match for my background. I have experience with drone component integration, autonomous robotics, sensor fusion, ROS2, PX4, Raspberry Pi/embedded platforms, OpenCV, YOLO, and real-time AI pipelines. I can help structure the system around: Raspberry Pi 5 + AI HAT+ 2 for onboard AI inference Real-time UAV detection and visual tracking OpenCV/YOLO-based perception pipeline MAVLink integration with ArduPilot Target position/velocity estimation and telemetry SITL simulation and hardware-in-the-loop validation Logging and GCS integration Robust failsafes and loss-of-target handling Safe stand-off following behavior rather than uncontrolled pursuit Clean, modular source code and deployment documentation I also have hands-on experience with AI-based 6D pose estimation for robotic manipulation, where I worked with deep-learning vision, RGB-D data, coordinate transformations, and precise pose estimation. This experience is directly relevant to building a reliable vision-to-vehicle tracking pipeline. Best regards, Hussein Ali

@huonghy89

Hi, I hope you are doing well. Very happy to bid your project because my skills are fitted in your project. I have strong experience with Python/C++, computer vision, real-time object detection and tracking, Raspberry Pi/edge AI deployment, telemetry, and embedded AI systems. I am also comfortable with ArduPilot, MAVLink integration, simulation/testing workflows, and developing reliable software with safety and failsafe requirements. I will develop and optimize the UAV detection and tracking pipeline for Raspberry Pi 5 and AI HAT+ 2, including real-time inference, target tracking, and telemetry logging. I will integrate the perception system with ArduPilot through MAVLink and implement constrained follow/stand-off navigation with explicit safety limits, loss-of-target handling, and failsafes. I will validate the system extensively in SITL before controlled deployment, provide the GCS interface, documentation, test results, and complete maintainable source code. If you send the message, we can discuss the project more. Thanks.

@KasjanovaEste

Hi there, the main goal is to build a real-time UAV detection and tracking system that can run reliably on Raspberry Pi 5 with AI HAT+ 2 and integrate with your ArduPilot stack. I’d structure the system around an optimized vision pipeline using Python/OpenCV and a suitable detection/tracking model, with MAVLink telemetry, logging, GCS communication, SITL validation and clearly defined failsafes. I think you’re aiming for a field-ready system that can progress from simulation to the physical quadcopter, so I’d keep perception, tracking, navigation logic and hardware interfaces modular and testable. I can provide the complete source code, integration documentation and reproducible build/setup process. M1: Vision + SITL integration, 8 days, ₹40,000 M2: Tracking + MAVLink + failsafes, 10 days, ₹55,000 M3: Hardware testing + documentation, 7 days, ₹35,000 Total: ₹130,000. Looking forward to working with you.

@Cruz1217

Running a deep‑learning model on a Raspberry Pi 5 can choke the CPU unless the inference is delegated to the AI HAT+ 2's accelerator. I’ll start by pruning a lightweight YOLOv5 model to fit the Pi’s memory and then bind it to OpenCV for frame‑by‑frame detection. From there I’ll hook the detection output into a Kalman filter for smooth tracking and push the coordinates over MAVLink to the ArduPilot controller. A common mistake is relying on raw camera frames without normalizing exposure, which leads to missed detections in bright sky. I’ll calibrate the camera pipeline and add adaptive thresholding so the model sees consistent input. You’ll get reliable real‑time UAV alerts, autonomous follow mode, and full source code ready for SITL testing.

@Hrolgar

Why detect-and-track first in simulation rather than on the airframe: what does the client already have running on the ArduPilot side, SITL configured with a matching frame, or would that need setting up as part of M1? For a Pi 5 plus AI HAT+ 2, the detection model needs to hit real frame rates on that NPU before any tracking logic is worth building on top of it, and the follow/stand-off behavior has to be proven against a simulated flight controller before it commands a real quad. MAVLink over pymavlink gives clean hooks into ArduPilot's guided mode for exactly that, position setpoints and velocity commands the tracker can drive directly, with the failsafe logic (link loss, target loss, geofence, battery) tested against a scripted SITL scenario before anything touches hardware. The build runs in that order. Detection and tracking get validated against recorded and live camera feed on the Pi first, tuned for the HAT's inference budget. Then the MAVLink bridge goes up against SITL with the same frame and parameters as the physical quad, running follow and stand-off patterns, forcing failsafe triggers on purpose to watch the recovery behavior. Only once that's solid does it move onto the airframe with telemetry logging live and a GCS view of tracker state, target position and mode. All source handed over at the end, structured so each milestone's output plugs into the next without rework. M1: SITL environment plus ArduPilot parameter match to the target airframe, MAVLink command/telemetry bridge proven in guided mode, 31875 INR, 4 days. M2: Detection and tracking pipeline on the Pi 5 + AI HAT+ 2, benchmarked for frame rate and latency against the target UAV signature, 34500 INR, 5 days. M3: Autonomous follow/stand-off logic driven off tracker output, failsafes for link loss, target loss, geofence and battery tested in SITL, 34500 INR, 5 days. M4: Hardware integration, telemetry logging, GCS interface, live flight validation, full source handover, 26625 INR, 4 days. What frame and flight controller firmware version is the quad running, and is there footage of the target UAV already, or does the detection model need training data collected from scratch?

@Abror86

Hi-Abror Here From Uzbekistan. "Build AI UAV Detection And Tracking" - I can develop real-time UAV detection using Python, OpenCV, deep learning, Raspberry Pi, and MAVLink integration. I can implement vision-based detection, object tracking, telemetry logging, GCS communication, SITL testing, failsafes, and MAVLink integration for reliable autonomous quadcopter operation. I will optimize AI inference for Raspberry Pi 5 and AI HAT+ 2, implement safe stand-off navigation, and provide complete source code. Could you share your current ArduPilot configuration and preferred AI detection model for initial integration planning? Looking forward to working with you.

@nichitav7

Hello, I can develop this as a complete companion-computer stack around Raspberry Pi 5 + AI HAT+ 2, integrated cleanly with ArduPilot through MAVLink. My background includes UAV flight-controller hardware, Jetson/embedded AI platforms, sensor integration, telemetry, and autonomous-system development. For this project I would structure the software into detection, tracking, MAVLink/GCS interface, navigation control, logging, and safety layers so each block can be tested independently. The workflow would be: validate the AI model and camera pipeline → implement real-time tracking → integrate vehicle state/control via MAVLink → add configurable stand-off/follow logic → implement geofence, link-loss and target-loss failsafes → verify in ArduPilot SITL before controlled flight testing. I would provide reproducible source code, configuration files, SITL test scenarios, telemetry/logging support, and documentation for deployment and future maintenance. Let's discuss. Best regards, Nichita

@larencea30

Hi, I reviewed your UAV AI tracking specification and understand this is a full system-integration project, not just object detection. I can develop the pipeline for Raspberry Pi 5 + AI HAT+ 2, including UAV detection, persistent tracking, target selection, bearing estimation, MAVLink/ArduPilot integration, safe stand-off following, telemetry logging, failsafes, and SITL testing. My approach would keep ArduPilot responsible for flight stabilization while the Raspberry Pi only sends bounded high-level navigation requests. I would also implement target-loss handling, operator override, geofence checks, health monitoring, and failure recovery as defined in your specification. I suggest milestone-based development: hardware bring-up → detection/tracking → MAVLink integration → SITL validation → controlled flight testing → final source-code/documentation handover. I can provide a detailed architecture, milestone schedule, and quotation after confirming the FC model, camera, target telemetry availability, and test setup. Best regards

@Ravan011patel

Hi, I’m a Robotics & AI Engineer with hands-on experience developing drone systems using Raspberry Pi, ArduPilot, MAVLink, and embedded hardware. I’ve also worked with Computer Vision and YOLO, including training a YOLOv8 model to detect diseases present on grape leaves. Your project strongly matches my experience in UAV development, AI-based detection, tracking, and autonomous robotics. I can work on the vision pipeline, object tracking, MAVLink integration, telemetry logging, SITL testing, and integration with the flight-control system. Please check my profile and portfolio for my previous drone and robotics projects. I can also share project videos, circuit diagrams, and previous work to demonstrate my practical experience. I’d be glad to discuss the system architecture and start with SITL testing before moving to the real hardware.

@tyronet2

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@InjectAI

I read the requirements carefully. This project is closely aligned with my Computer Vision, UAV, embedded AI, MAVLink, and simulation experience. I can build the perception and autonomy stack around Raspberry Pi 5 + AI HAT+ 2 while keeping safety and SITL validation central to the implementation. My approach would be: • Real-time UAV detection using YOLO/PyTorch with optimized inference • Object tracking with persistent target IDs and robust loss/reacquisition handling • MAVLink communication between Raspberry Pi and flight controller • Target position/relative-motion estimation for controlled follow or stand-off behavior • Explicit safety boundaries, geofencing, target-loss behavior, manual override, and failsafes • Telemetry and event logging for debugging and post-flight analysis • Lightweight GCS interface for monitoring target, vehicle, and system status • ArduPilot SITL testing before hardware deployment • Performance profiling and optimization for Raspberry Pi 5 + AI accelerator • Clean modular source code with configuration separated from core logic I have stronger hands-on experience with PX4 than ArduPilot specifically, so I would not misrepresent that. However, the MAVLink-based architecture and SITL workflow are directly relevant, and I can adapt the integration to your ArduPilot setup.

@hexabitzprojects

Hi, This project strongly matches my **16 years of R&D experience in AI/ML, computer vision, UAV/robotics and embedded systems**. I have completed **AI Dashcam, ANPR, YOLO-based detection, machine-vision and embedded motion-control projects** involving real-time vision and control. I can develop the **Raspberry Pi 5 + AI HAT+ 2** vision system for real-time UAV detection and tracking, with **OpenCV/Python/PyTorch, MAVLink and ArduPilot** integration. The system can include target detection/tracking, position and motion estimation, MAVLink telemetry, GCS communication, event logging and configurable tracking parameters. For autonomous follow/stand-off operation, I can implement controlled guidance with configurable distance limits while keeping **ArduPilot failsafes and manual override** authoritative. I will use **ArduPilot SITL** extensively for simulation and validation before hardware testing, including loss-of-target, communication-loss and failsafe scenarios. **Deliverables:** complete source code, vision/tracking pipeline, MAVLink integration, follow/stand-off logic, telemetry logging, GCS interface, failsafe handling, SITL test setup and documentation. My previous **YOLO, AI Dashcam, ANPR, machine-vision and embedded motion-control projects** are directly relevant to this application. Best regards, **Subhash Suman** AI/ML | Computer Vision | UAV | ArduPilot | Embedded **16 Years R&D Experience**

@Visharaa2024

Hi, For a system like this, I’d treat the Raspberry Pi/AI HAT+ 2 as a companion-computer perception layer rather than mixing detection logic directly into flight-control code. The key is establishing a reliable pipeline from UAV detection and tracking through MAVLink telemetry, with explicit safety states and supervised autonomous behavior. I’d approach it in stages: first validate the Raspberry Pi 5 + AI HAT+ 2 inference pipeline, then integrate tracking and MAVLink telemetry, build the GCS/logging layer, and validate the complete behavior in ArduPilot SITL before controlled hardware testing. Target-loss handling, manual override, telemetry health and other failsafes would be treated as first-class requirements. We can provide structured Python-based AI/integration work, clean source, documentation and reproducible testing, while keeping the architecture modular for future model or hardware changes. One important question: do you already have the camera specification, target UAV type, and ArduPilot SITL environment available? If this aligns with your requirements, we can begin with the hardware/SITL feasibility phase. Best regards, Deepak.

@nakabirik

Hello, I have read your project requirement description carfully! The key is keeping real-time UAV detection, tracking and MAVLink telemetry reliable on the Raspberry Pi 5 while ensuring autonomous behaviour always remains bounded by explicit failsafes. I can work across Python, OpenCV, embedded integration and API/data-processing layers. I’d separate detection, tracking, telemetry and navigation into clear modules, with confidence thresholds, stale-target handling and loss-of-track states preventing unsafe commands. SITL should be used before hardware testing to validate MAVLink messages, follow/stand-off logic, telemetry logging and failsafe transitions under reconnect and sensor-loss conditions. I’ll also keep the GCS interface and configuration parameters documented so the system can be tuned without modifying core logic. Could you share the supplied specification document and confirm which detection model/runtime is expected on the AI HAT+ 2? Looking forward to working with you. Muheebwa