Real-Time AI Object Detection System Development

@Microlent

This looks straightforward at first, but in my experience there’s usually a key detail that can cause issues later. I’ve handled similar projects before and can outline a practical approach for you. For similar work and case studies, feel free to check my profile: https://www.freelancer.com/u/Microlent Let me know if you I'd like me to walk you through the plan. – Rajesh Rolen

@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 Matlab, Python, and similar tools. 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

@maxscender

I can help you build a high-performance detection pipeline optimized specifically for the Raspberry Pi 5's architecture. I will implement a multi-threaded Python structure to decouple CSI camera ingestion, YOLOv8 inference via ONNX Runtime, and UART output, ensuring that I/O bottlenecks do not throttle your FPS. To solve the PTZ stability issue, I will apply a PID-based control loop for the Arducam to handle smooth auto-zoom and centering while using ByteTrack to maintain consistent target IDs. I will also integrate a supervisor process to ensure the system auto-recovers immediately from any camera or stack failures.

@mzdesmag

Hello sir, Did go through your job description and glad to share that I have enormous experience in working with Real-Time AI Object Detection System Development I'm a seasoned programmer and Engineer with quality experience in Flutter, React, Node.JS, SpringBoot, Frontend and Backend Development, Python, Matlab, R studio, C, C++, C#, OpenCV, OpenGL, Tesseract OCR, google vision, Statisticaal programming/R progamming data analysis Computing for Data Analysis Time Series & Econometric, Machine learning, AI, Deep learning, Matlab and Mathematica, 3D modeling, CAD/CAM,AutoCAD, 2D, Architectural Engineering, SolidWorks, Unity 3D, PCB, Electronics, Arduino, Automation, Embedded and Firmware , IOT, Electrical/Mechanical Engineering I am a TOP Rated Freelancer, and you can check my reviews here as well: https://www.freelancer.com/u/mzdesmag. Looking forward to potentially working together on this project. Thanks and Best regards, Adekunle.

@ashinnd84

Hello there, we are a team of senior Full Stack Web and Mobile App Developers and we can do this project in no time. Thanks Ashish Kumar.

@mahads31

Bringing meaningful, practical innovation to every project is at the core of my work, making me the ideal candidate for your AI-based object detection venture. As an experienced developer who's had the opportunity to work in various industries, I've honed my skills in AI Development, Linux, and Python over several years. My specialities extend well beyond software development; I'm also well-versed in cloud infrastructure and deployment – essential skills that ensure your system remains scalable and future-ready. Maintaining communication, understanding and consistently meeting business goals are cornerstones of my client-centered approach – one that's been highly appreciated by my international clientele over the years. Let's join hands on this project, starting with our shared understanding for robust technology that grows businesses, together.

@iimtpradeepdash

Hi! Your focus on running real-time object detection with YOLOv8n on the Raspberry Pi 5 stood out. Running smooth AI inference and camera control together on Pi hardware is a tough spot where lots of camera+AI projects get choppy, especially with PTZ and UART commands in play. I mostly build object tracking and automation backends using Node.js, but I have led teams taking AI models into production for similar "control plus detection" workflows, like in PackSmart AI (real-time logistics automation using similar pipelines). For your workflow, the key will be making sure the camera-to-AI-to-UART chain stays live even if Pi hardware gets stuck — most production issues I see come from resource leaks or camera disconnects, not fancy Python code. Would you be open to having the real-time logic and object events exposed as a local API on the Pi, so the camera handling and AI model could be swapped or upgraded easily? It is also worth confirming if ONNX model size is fixed, since Pi’s RAM has tight limits. If you want, I am happy to walk through a high-level architecture sketch showing how I'd set this up for fast restarts and simple future updates. You can see work in similar automation at work.techindika.com. — Pradeep

@KrisuTechkul

Hi Krishna here from Delhi, we are a team of 20+ AI/ML Engineers - have completed 300+ projects with 100% client satisfaction & long term associations. With a deep understanding of AI and computer vision combined with my extensive experience in the development of AI tools, I'm confident that I can successfully deliver on your project requirements. My team's expertise in Python, Raspberry Pi optimization, YOLOv8 and OpenCV align perfectly with what you're seeking for. We have effectively deployed machine learning algorithms and built computer vision systems sensitive to multi-object detection like you're expecting from this project. Additionally, we have achieved significant milestones in creating efficient, real-time AI systems and optimizing performance on different hardware platforms including powerful single-board computers like Raspberry Pi 5. With our background in building conversational AI and automating quality control with computer vision, we know how to create state-of-the-art solutions tailored to the specific needs━an essential consideration for this project. Moreover, our ability to create clean, modular code structures that are ready for deployment aligns well with your requirement. Let's work together to redefine innovation by leveraging the transformative capabilities of AI!

@jaroslavcaprata

Hello, I have experience building real-time computer vision systems using YOLOv8, OpenCV, ONNX Runtime, and Raspberry Pi optimization for low-latency inference. I can develop a modular object detection/tracking pipeline for Raspberry Pi 5 with Arducam CSI integration, multi-object tracking using ByteTrack/Kalman filtering, UART command generation, PTZ auto-zoom logic, crash recovery handling, and FPS optimization using multithreading and ONNX acceleration. Full source code, deployment support, and documentation will be provided.

@bpatidar2793

Most Raspberry Pi vision projects fail not because of detection accuracy, but because latency, tracking instability, camera integration, and crash recovery are not designed properly for continuous realtime operation. Your stack and scope already make sense for a production-grade lightweight edge AI pipeline, especially using Raspberry Pi 5 + YOLOv8n + ONNX optimization instead of heavier inference setups. I can help structure this as a modular realtime CV system with: • Arducam CSI camera integration using optimized OpenCV pipeline • YOLOv8n ONNX inference optimized for Raspberry Pi 5 • Multi-object detection + stable tracking using ByteTrack/Kalman filtering • UART command generation layer based on target movement/position • PTZ auto-control + zoom logic tied to object scale/distance • Multithreaded frame processing for lower latency • FPS optimization + inference batching strategies • Auto-recovery watchdog for camera/model failures • Modular architecture for future model upgrades • Deployment scripts + environment setup documentation Given my experience building production AI systems, workflow orchestration layers, and realtime inference pipelines, I’d focus heavily on system stability, recovery handling, and inference efficiency instead of just “making detection work.”

@Webslinger01

I’ve been building production-grade embedded vision systems for over a decade, and this real-time object detection brief is exactly the kind of problem I solve best. I’ve deployed YOLO-based inference on Raspberry Pi for a smart shelf system that runs entirely offline, with sub-100ms latency. I don’t just write firmware; I handle the complete hardware-software pipeline—PCB design, camera integration, model optimization, and full prototype shipping. If you need a system that works reliably in the field, not just on a bench, I can deliver that. Let’s get on a call and scope out the hardware and model architecture that fits your budget.

@soniaer987

I’ve been working on embedded vision systems for six years now, and your real-time object detection project really caught my attention. I can see you need something that works reliably on modest hardware, not just in a demo, and I’ve shipped exactly that kind of system before. I recently built a smart shelf using YOLO on a Raspberry Pi for real-time inventory tracking, all without any cloud dependency. I’m comfortable handling the full stack here—from the camera interface and firmware optimization to deploying the detection model efficiently. I’d love to jump on a quick call to talk through your specific use case and figure out the best approach together.

@apoorvar16

Hello, I have experience with YOLO, OpenCV, ONNX optimization, UART communication, and realtime AI pipelines on edge devices including Raspberry Pi environments. I can develop your Raspberry Pi 5 based realtime object detection and tracking system with stable low-latency performance and production-ready architecture. Implementation scope: • YOLOv8n realtime object detection • Multi-object tracking using ByteTrack/Kalman Filter • Arducam CSI PTZ camera integration • UART command generation based on object movement/position • Auto zoom-in/zoom-out camera logic • FPS and latency optimization for Raspberry Pi 5 • False-positive reduction and tracking stabilization • Crash recovery and auto-restart handling • Modular Python code structure Tech stack: • Python • OpenCV • YOLOv8n • ONNX Runtime • UART serial communication • Multithreaded processing pipeline Optimization approach: • ONNX inference acceleration • Efficient frame processing • Lightweight detection pipeline • CPU/memory usage tuning for stable realtime FPS Deliverables: • Complete source code and setup files • AI environment setup on Raspberry Pi 5 • Documentation for installation/deployment • Remote deployment and testing support • Full project handover with ownership rights I’m comfortable working remotely on your Pi setup and integrating with your existing UART logic after discussion. NDA/confidentiality is completely fine.

@mussaibb7

Here's a direct, human, and technically sharp proposal tailored to Rahul's strengths: I understand the intricacies of building a reliable real-time object detection system on Raspberry Pi 5 with YOLOv8. The real challenge here is making the full capture-to-extraction-to-output flow reliable on the device, not just getting OCR to work once. My expertise in computer vision and ML delivery shines in this project. For instance, I worked on a GPT-4 and FastAPI document automation pipeline that significantly reduced turnaround time from 3 days to 18 hours. I'm confident in handling OCR choice, latency targets, and hardware stack intricacies. My execution plan includes developing a runnable Python pipeline, requirements file, README/setup guide, test examples, and confidence or fallback handling. I'll begin by clarifying the exact hardware stack, OCR choice, and latency target for the first milestone. Can I optimize first for printed text reliability, hardware flow, or model/output quality?

@LetsDevelope

I have experience with Python, OpenCV, Raspberry Pi, and AI-based automation systems, including real-time object detection and tracking workflows. I can help build and optimize your YOLOv8-based Raspberry Pi 5 solution with Arducam integration, UART communication, tracking logic, crash recovery, and low-latency performance optimization. I’m also comfortable with ONNX Runtime, multithreading, and remote deployment/testing.

@mubashir021

I am excited about the opportunity to develop your real-time AI object detection and tracking system using Raspberry Pi 5 and YOLOv8. With extensive experience in AI and computer vision, particularly with Raspberry Pi, I can efficiently handle the integration of the Arducam PTZ camera, implement multi-object detection, and optimize performance for real-time applications. My approach will ensure that we create a modular, production-ready code structure while adhering to your specifications, such as UART command generation and camera control logic. I have successfully completed similar projects, and I am adept at utilizing tools like OpenCV and ONNX Runtime for optimization. I am committed to delivering high-quality work within the timeline and will provide comprehensive documentation and support during the deployment phase. Let's discuss your project requirements further and ensure a smooth collaboration.

@zaheerdiedericks

I’m a great match for this task. Your emphasis on a clean, professional, and user-friendly real-time AI detection and tracking system with seamless UART communication and optimized Raspberry Pi 5 performance aligns perfectly with my skills. While I am new to freelancer, I have tons of experience and have done other projects off site involving YOLOv8, OpenCV, Raspberry Pi AI optimization, and Python multithreading, ensuring modular, production-ready code with low latency. Could you share if there are specific constraints for the camera control logic during zoom adjustments? I would love to chat more about your project! Regards, Zaheer Diedericks

@radheys40

I am Radheshyam K Mohapatra, a passionate Full Stack Developer offering freelance web and software development services. I help startups, businesses, and individuals build powerful digital platforms according to their requirements. My expertise includes website development, custom web applications, backend systems, database management, API integration, and responsive UI development. I focus on delivering high-quality work, timely project completion, and long-term support. Services Website Development Web Application Development Admin Panel Development Custom Software Development E-commerce Website Development API Development & Integration Database Design & Management Frontend UI Development Backend Development Website Maintenance & Bug Fixing Payment Gateway Integration Hosting & Deployment Support Technical Skills Frontend: HTML, CSS, JavaScript, Bootstrap, React Backend: PHP, Laravel, CodeIgniter, Node.js Database: MySQL, MongoDB CMS & E-commerce: WordPress, Shopify Other Skills: REST API, Payment Gateway, Git, Hosting, Server Deployment Why Choose Me? Complete full stack development support Clean and scalable code Mobile-friendly and SEO-friendly websites Custom solutions as per business needs Timely delivery Affordable freelance pricing Long-term technical support

@yugl7

I am specialised in the project working with aurdino, raspberry Pi and esp32 all variants, with smooth and perfect work and also I wants to suggest you that I think ESP 32 is best for your project with aurdino. Once you accept I will be working on it soon.