Edge Device Aerial Detection - 03/02/2026 12:21 EST

@quan9824

https://www.freelancer.com/projects/raspberry-pi/Powered-Monitoring-Prototype-Development/reviews ## EXPERT ##(Python and Raspberry PI, Machine learning) Hello, How are you? I’ve completed several raspberry pi projects before successfully. Recently, I developed Smart Dashcam using Raspberry PI and USB Dongle in Netherland. You can check this in my portfolio. I can upload my previous works too.. I am sure and I can start immediately. Awarding me will be the fastest way to complete your task with the best rates possible. THANK YOU.

@hotanloc

Hello Sir.(YOLO EXPERT) My core skill is in OBJECT DETECTION, TRACKING and COUNTING. I have expert skill in image processing which detect object and count object from image and video of CCTV camera. In this project, I will train model to get weight using my own training model. After that I will develop detect code to perform detection object base on traned model. And then I will analysis detected object and display that result. I am expert in these fields (YOLO, OCR, OpenCV, Tensorflow, PyTorch, Keras, ML/DL model). I have full experiences in this project with my full knowledge of ML/DL which train annotated image and predict base on trained model. After that I will count number of object with some back processing using opencv. I am sure this project and I can finish this task with high quality. Please send me message to discuss your project in more details. Thanks.

@Hemantt841

You’re looking to develop a compact, fast object-detection model that runs efficiently on edge devices like Jetson Nano or Raspberry Pi, specifically processing aerial drone images with reliable detection of people and critical infrastructure. The model must outperform YOLOv9c while supporting real-time inference after optimization, and it should remain adaptable for future applications like agriculture or disaster response. With over 15 years of experience and more than 200 projects completed, I specialize in Python, Linux, machine learning, and edge computing, which perfectly align with your needs. My background in deep learning and computer vision ensures I can deliver optimized object detection models tailored for resource-constrained hardware such as Raspberry Pi and Coral devices. I will start by selecting and training a lightweight architecture—likely EfficientDet-Lite or a carefully optimized YOLO variant—then quantize and convert it to TensorRT or TFLite for edge deployment. The workflow will include a prototype phase, thorough benchmarking for mAP and FPS, and a clean Python inference script with documentation. I estimate this can be completed within 4–6 weeks, including iterative optimization and reporting. Feel free to reach out so we can discuss how to tailor the solution to your specific dataset and deployment needs.

@Visharaa2024

Hello, I can develop a compact, edge-optimized object detection model for drone aerial imagery that runs efficiently on Jetson Nano, Raspberry Pi, or Coral devices. I will use lightweight architectures such as YOLOv8-nano, EfficientDet-Lite, or MobileNet-SSD, then apply quantization and TensorRT/TFLite optimization to achieve real-time inference while maintaining strong detection accuracy for people, vehicles, and infrastructure. The delivery will include an edge-ready model (TensorRT/TFLite/ONNX), a Python inference script with one-command execution, clear documentation, and an evaluation report with mAP, FPS benchmarks, and a demo clip on aerial test data. I will follow a prototype → optimization → deployment workflow to ensure the system performs efficiently on edge hardware and remains flexible for future applications like agriculture or disaster monitoring. Looking forward to working with you.

@tariza1

Hi, I'm Tariz, an AI and Computer Vision Engineer with 3+ years of experience across a wide range of CV projects surveillance systems, driver behavior detection, ANPR, egg counting and classification, workplace safety detection, embryo detection, solar panel analysis, and more. A good number of these were deployed directly on edge devices like Raspberry Pi and ESP32-CAM, running in real environments without any internet dependency. I've benchmarked and compared multiple model architectures across different sizes Nano, Small, Medium, Large and exported them in .pt, ONNX, and TensorRT depending on the target hardware. Optimization, quantization, and getting the best FPS without sacrificing accuracy is work I've done repeatedly, not something I'm figuring out for the first time. The deliverables you've listed edge-ready model files, a clean Python inference script, evaluation report with mAP and FPS , are things I've produced before as part of actual deployments. If you'd like, I can share a demo or prototype from previous work so you get a clear picture of the output quality and if needed, I can put together a quick prototype on your use case as well, before we proceed any further. My portfolio is at tariz.in. Tariz

@murtuza90

Combining my 13+ years of extensive experience in ???????? ????????? with your vision for a high-performing, real-time object-detection model, I assure you of a complete end-to-end solution from prototype to maintenance. Having been thoroughly seasoned with ?-2-Edge development, I can develop and optimize the best architecture (like YOLOv8) to transcend the limitations of traditional techniques. To ensure edge-readiness, I'll deliver a compatible model file (like TensorRT), an inference script in Python for straightforward integration & a detailed README for ease-of-use. Given that your project entails identifying specific objects like people, vehicles & infrastructure elements, you would need accuracy and efficiency in object detection. My proficiency in Python assures you the precision required, as well as the ability to adapt and apply powerful frameworks like TensorFlowLite and ONNX. With my expertise in Linux systems, your edge board will be reliable and efficient as it processes aerial images from drones. Moreover, I understand that your project is not just about current surveillance but also future applications in agriculture or disaster-response scenarios. I guarantee that the system I develop will be flexible enough to handle these scenarios too. I'm confident that my professional history filled with successful project deliveries built on quality, scalability, and effective communication, can deliver what you envision!

@mfilipovacfl

Hi, I understand you're looking for a fast, reliable object-detection model for edge devices like Jetson Nano, Raspberry Pi, or Coral, with the immediate goal of surveillance and the flexibility to apply it in agriculture or disaster-response. I have experience developing and optimizing object-detection models for edge devices, including deploying YOLO-based architectures and alternatives like EfficientDet-Lite and MobileNet-SSD. For your project, I would propose starting with a tailored architecture that prioritizes people and critical infrastructure detection, optimizing it for real-time inference by quantizing or converting the model to formats like TensorRT, TFLite, or ONNX. I will deliver: * An optimized edge-ready model file * A Python inference script with a one-command launch and clear README * An evaluation report with mAP, FPS, and a short demo on a held-out aerial dataset Let’s discuss further how we can structure the prototype and optimization stages. Best regards, Mihailo

@yanezhans

Hey! I'm Yanez, senior full-stack dev with 7+ years in computer vision & edge AI, building real-time object detection for drones on Jetson Nano, Raspberry Pi, Coral, etc. I've shipped aerial surveillance models for security, ag monitoring, and disaster scenarios, always squeezing max FPS + accuracy from limited hardware. Leveraging my edge experience, I've beaten YOLOv9c on aerial benchmarks using YOLOv11 (2025/26's edge champ), with superior small-object detection, faster inference, and killer quantization. In the "AeroGuard Urban Drone" project, I deployed YOLOv11n on Jetson Nano: fine-tuned on VisDrone + UAVDT, exported to TensorRT, one-command Python script with OpenCV live feed. Biggest hurdle: tiny people/infra (poles, wires) from altitude + sub-30ms latency. Fixed via mosaic aug, auto-anchor tuning, INT8 quant, and TensorRT dynamic shapes. Hit ~38 FPS @ 640×640, mAP ~5-8% above YOLOv9c on held-out drone sets, super reliable on people & critical infra while catching vehicles. Fully reusable for agri/disaster later, open datasets only (no fees). Deliverables: TensorRT file, inference script + README, eval report (mAP/FPS), demo clip. Milestones: prototype → optim → handoff. Yanez

@HemaHema777

Hello, ​I can deliver a high-performance, lightweight object detection model specifically optimized for Jetson Nano and Raspberry Pi that outperforms YOLOv9c in speed while maintaining high reliability for aerial imagery. ​Why choose my solution? ​Architecture: I will implement YOLOv8-Nano optimized via TensorRT or TFLite. This ensures maximum FPS (Frames Per Second) on edge hardware, which is critical for real-time drone surveillance. ​Aerial Specialist: I will fine-tune the model to recognize small objects (people and infrastructure) from a top-down perspective, ensuring the model remains robust in diverse environments (Agriculture/Disaster response). ​Production-Ready: You will receive a clean Python inference script with a "one-command launch" and a comprehensive evaluation report (mAP and FPS metrics). ​My Plan: ​Phase 1: Selection and quantization of the best model (INT8/FP16) for your specific hardware. ​Phase 2: Testing on aerial datasets to ensure reliability for people and infrastructure. ​Phase 3: Final hand-off with a clear README and a demo clip. ​I am a Python & Linux specialist ready to start immediately. Let’s discuss your specific hardware (Jetson or Pi) to tailor the optimization perfectly. ​Best regards, [Ibrahim Mohamed]

@WebSpaceSolution

I can design and deliver a compact, high-performance edge-ready object detection pipeline optimized specifically for aerial imagery and real-time deployment on Jetson Nano, Raspberry Pi, or Coral devices. My approach would start with a strong lightweight backbone (e.g., YOLOv8-n/s with architectural tuning, EfficientDet-Lite variants, or a custom MobileNetV3-based detector) and benchmark against YOLOv9c under identical aerial validation conditions. From there, I’ll apply structured pruning, INT8 quantization, TensorRT/TFLite conversion, and hardware-specific optimizations to maximize FPS without sacrificing mAP—especially for people and critical infrastructure classes. You’ll receive: • Edge-optimized model (TensorRT, ONNX, or TFLite depending on target board) • Clean Python inference script with one-command execution • Evaluation report with mAP, per-class performance, FPS benchmarks, and demo clip • Clear documentation for reuse in surveillance, agriculture, or disaster response scenarios I’m comfortable staging this through prototype → optimization → final deployment milestones and will disclose any dataset licensing requirements upfront.

@Apartechnologies

As a seasoned software engineer with proficiency in Python and software architecture, I would be an ideal choice for your Edge Device Aerial Detection project. My extensive experience allows me to not only implement efficient object-detection models but also optimize them for edge devices like Jetson Nano, Raspberry Pi or Coral. Our digital agency, Apart Technologies, takes considerable pride in creating solutions that "stand apart". This means that the final product we deliver isn't only functional but also future-proof and adaptable. The focus of your specific project is on real-time aerial surveillance - a skill we have cultivated through successful implementations with similar objectives. We understand the criticality of your project in detecting human presence and infrastructure elements accurately. With the mentioned backgrounds, I can confidently assure you a high mAP, fast FPS and most importantly - a real-time inference on edge devices. I guarantee the delivery of an edge-ready model file along with a one-command launch Python inference script + a detailed README to ensure ease of use.

@sandip761

Hello, Your requirement aligns closely with my background in real-time computer vision and edge AI systems. I’m an AI Engineer with 7+ years of experience in object detection, video analytics, and edge optimization. I’ve deployed YOLO/SSD-based pipelines on Jetson platforms using ONNX and TensorRT, improving FPS by 20%+ through inference tuning and model optimization. I’ve also worked on safety-critical ADAS detection systems. Technical Approach Architecture Benchmarking Instead of YOLOv9c, I would benchmark: - YOLOv8n/s (TensorRT optimized) - YOLOv7-tiny (Nano-friendly) - EfficientDet-Lite (for Coral TPU) - RT-DETR-lite (if accuracy gain justifies cost) For aerial imagery: - High-resolution tiling for small objects - Anchor/head tuning - Drone-specific augmentation (scale, rotation, altitude) Edge Optimization - PyTorch → ONNX → TensorRT (FP16 / INT8) - TFLite + Edge TPU quantization (Coral) - TFLite acceleration for Raspberry Pi Goal: Real-time (≥20 FPS board-dependent) with strong person & infrastructure recall, outperforming YOLOv9c. Deliverables - Edge-ready model (TensorRT / ONNX / TFLite) - One-command Python inference script + README - Evaluation report (mAP, FPS, demo on held-out aerial set) Datasets: VisDrone, UAVDT, or augmented COCO (license clarified upfront). Happy to stage this through prototype → optimization → final handoff. Best regards, Sandip Senapati

@sujathap74

Hi, I have hands-on experience building real-time object detection systems using YOLO and deep learning models in Python. I previously developed a Smart Traffic Detection System using Computer Vision, which involved detecting vehicles and people from live video streams. For your edge-based aerial detection system, I propose using YOLOv8 or a lightweight EfficientDet model optimized with TensorRT or ONNX for real-time performance on Jetson Nano or Raspberry Pi. My approach: • Model selection and benchmarking • Quantization and TensorRT optimization • FPS and mAP evaluation on aerial dataset • Clean Python inference script with one-command execution • Documentation + demo clip I can ensure reliable detection of people and infrastructure while maintaining real-time edge performance. I would be happy to discuss your target FPS and hardware constraints before starting. Regards, Sujatha

@DavidOkosa

Hey Hey, would love to help develop your project, edge/constrained real-time ML systems are right up my alley as well with projects achieving 60+fps coupled with high accuracies.