AI Camera Piece & Person Counter

@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

@quan9824

Hi, I can build a robust AI vision system tailored specifically for production-line monitoring with reliable item counting and operator detection, optimized for real-time edge deployment. The system will use a fine-tuned YOLOv8 (or equivalent PyTorch-based model) trained on your manufactured parts and operator scenarios, ensuring strong accuracy in controlled factory conditions rather than generic surveillance use cases. For real-time inference, I will implement an OpenCV or GStreamer pipeline that processes your IP camera feed efficiently and outputs two structured signals per frame: accurate item counts and a binary operator-present flag. The design will be optimized for stability and low latency so it can run continuously in a production environment. On top of this, I will build a lightweight API or dashboard that allows live monitoring of counts, system status, and retrieval of historical logs for daily reporting and traceability. The deliverable will include a fully documented training and deployment pipeline so your internal team can easily retrain the model when adding new SKUs or changing camera setups. This will include clear steps for dataset updates, retraining, and redeployment on edge hardware. I have experience in YOLO-based detection systems and industrial computer vision pipelines, including real-time inference and factory-style monitoring setups where reliability is critical. Best regards, Doan

@ducthodg

Hi, I can build this as a practical factory vision system, not a generic surveillance tool. I have experience with real-time computer vision using YOLO/OpenCV pipelines, object counting logic, camera stream processing, and lightweight dashboards/APIs. For this project, I would focus on stable item counting, avoiding double-counting, and reliable operator-presence detection under real shop-floor conditions such as motion blur, lighting changes, occlusion, uniforms, and different camera angles. My approach would be: Review sample camera footage/images and define the counting zone, object classes, and person-detection rules. Fine-tune a YOLOv8/YOLO-based model on your manufactured parts and shop-floor environment. Build an edge-ready Python/OpenCV or GStreamer pipeline for IP camera input. Add tracking/counting logic so each item is counted once when it crosses the defined line/region. Expose live results through a small API/dashboard with current count, person-present status, and daily logs. Provide clear setup documentation so your team can retrain or redeploy when cameras or SKUs change. The key deliverable will be a robust working system with source code, trained model weights, deployment instructions, and logging. I can also include a calibration/config file so camera changes do not require code changes. I would be happy to review your sample footage first and suggest the best model/tracking setup before development starts.

@shadabkhan92

Building on our deep experience in AI and enterprise software integration, our team at [Your Company Name] is uniquely poised to deliver the precise solution you're seeking. With an emphasis on robust and scalable production infrastructure, our track record demonstrates that we don't just develop prototypes or proofs of concept; we build cohesive systems that can handle the demands of real-world use cases. In fact, we have a specific expertise in factory analytics and image segmentation, both of which are directly relevant to your project. Our familiarity with industrial environments has given us unique insights into the specific challenges and nuances of manufacturing processes, enabling us to efficiently fine-tune AI models for highly accurate piece counting. Additionally, our ML prowess ranges from popular deep learning frameworks like TensorFlow to classic approaches like OpenCV—an ideal toolkit for your particular needs.

@mohjib092

Hello, I can build a robust AI vision system to count items on your production line and detect operator presence in real time. I’ll use a fine-tuned YOLOv8 model trained on your specific parts and shop-floor scenarios, with an edge-ready pipeline (OpenCV/GStreamer) that outputs accurate item counts and a person-present flag. I’ll also provide a lightweight dashboard/API for live monitoring and logs, along with clear documentation for easy redeployment. I have experience with computer vision and real-time detection systems, and I’ll focus on accuracy, stability, and scalability. Happy to discuss your setup and get started.

@SmartCodeDev

Hello, I can build your AI vision system for accurate item counting and operator presence detection on a production line. I will train a custom model using YOLOv8 or similar, fine tuned on your product images and real shop floor conditions. The system will run on an IP camera feed using OpenCV or GStreamer and process frames in real time. It will output reliable item counts and a clear person present status for each frame. I will include edge ready code optimized for stable performance on local hardware. A lightweight dashboard or API will provide live monitoring and daily log access. The solution will be designed for easy retraining if you change cameras or add new products. You will receive full documentation and deployment steps for your internal team. I have experience with computer vision and real time detection systems and can ensure strong accuracy and robustness.

@devanshkhandekar

Hi,I’m an Applied ML Engineer(6+ yoe) with hands-on computer vision experience,including real-time detection,tracking,OCR,safety monitoring,& industrial-style video analytics I will implement a YOLO-based detection system (v8/v11) fine-tuned on your specific parts,integrated with ByteTrack or DeepSORT to ensure unique counting & prevent frame-to-frame duplication Relevant Experience: -Visual Inspection:Developed production-grade YOLO/OpenCV pipelines for object detection,defect inspection & event logging -Real-Time Analytics:Engineered RTSP/IP camera workflows optimized for frame skipping, FPS tuning & unstable stream handling -High-Accuracy Tracking:Built OCR/ANPR & sports analytics systems where counting consistency & timestamped logs were critical My approach would be: >>Collect/sample frames from your camera angle & SKUs >>Label manufactured parts + operator/person classes >>Fine-tune YOLO & validate precision/recall under shop-floor lighting,motion blur,occlusion,& clothing variations >>Add line-crossing/zone-based counting so each item is counted once >>Add a boolean person_present flag based on operator-zone occupancy >>Deploy edge-ready inference using Python,OpenCV/GStreamer,ONNX/TensorRT >>Provide FastAPI endpoints/dashboard for live count,person status,& daily logs >>Document retraining steps for new SKUs/camera changes Final handoff would include trained weights,inference code,API/dashboard,logging module,deployment guide,& test cases for your in-house team

@mahads31

As an agile and client-centered software developer, I'm well-equipped to build your AI-driven vision system that precisely detects and counts manufactured items while simultaneously monitoring the presence of operators. Boasting experience in diverse sectors and global clients, I've fine-tuned my skills in transforming complex requirements into clean, reliable, and high-impact digital solutions. My proficiency in C and C++ programming, along with my sound knowledge of Software Architecture, aligns perfectly with the technical needs of this project. Moreover, I'm well-versed with utilizing machine learning libraries such as YOLOv8 and TensorFlow which will prove invaluable in building your trained model for object identification. Furthermore, my expertise extends to automation, integrations, smart workflows, and cloud infrastructure design – all vital aspects needed in deploying a robust model like yours. My approach centers around scalability - which means that not only can I provide the lightweight dashboard or API endpoints you need for your real-time monitoring but I can also ensure the setup documentation is clear and concise allowing for easier redeployment if needed.

@albertc1996

Hi I have read your project description carefully and understand the core requirement: a production-ready vision system that delivers reliable piece counting and operator presence detection, not generic surveillance. My approach prioritizes robustness over speed, using a fine-tuned YOLOv8 model with tracking-based counting logic to avoid double-counts or misses. The person-presence flag will include temporal smoothing to handle brief occlusions or pose changes. I will deliver edge-ready code that runs on your IP camera feed via OpenCV/GStreamer, exposing live counts and operator status through a lightweight dashboard or REST API. Daily logs will be stored for easy retrieval. Full documentation will be provided so your team can retrain the model if you swap cameras or add new SKUs. For assuring you of the progress of the project, I will keep you informed of the project progress at the end of every day. With prior experience in factory analytics and real-time detection systems, I can deliver a solution you can trust. Timeline is flexible—I prioritize quality. I look forward to discussing your specific camera setup and product images. Questions: What is the typical line speed and approximate physical size of the manufactured items? Could you share a few sample images or describe the lighting conditions at the station? Best regards, Albert C.

@alluankit01

Hi, I can build an AI vision system for your production line that counts manufactured pieces in real time and also detects whether an operator is present at the station. I understand the key requirement is reliability in a factory environment, not a generic surveillance model. I’ll start by reviewing sample camera footage/images, then fine tune a YOLOv8 or similar model for your specific parts and typical operator poses. The system will process the IP camera feed through OpenCV or GStreamer and output live item counts plus a simple person present true/false status. I can also add a lightweight dashboard or API endpoint showing current counts, operator status, timestamps, and daily logs for reporting. The code will be edge ready and documented so your team can retrain or redeploy it if you change cameras, lighting, angles, or add a new SKU. Please share sample production line images or video clips first so I can confirm the best counting method and model approach. Best regards Ankit

@manoval09

Hi, You need real-time object detection on a production line — counting pieces and people simultaneously. The challenge here is getting low-latency accuracy without breaking the budget. I'd approach this with YOLO v8 (pre-trained for person detection) running on your camera feed with custom fine-tuning for piece detection. OpenCV handles the streaming and counting logic; MQTT or REST endpoints pipe the counts to your dashboard. If your camera is USB or network-connected, setup is straightforward; if you need edge inference (Raspberry Pi, Jetson), I'll use TensorFlow Lite. The key is choosing the right hardware-software pairing first — that determines everything else. Here's what I need to move forward: Is the camera feed already streaming somewhere, or do I need to integrate with the physical camera directly? And how strict is your accuracy tolerance — are you targeting 95%+ precision on both object types, or is 80% acceptable for the MVP? Best regards, Val

@mubashir021

I understand the critical importance of an accurate AI-driven vision system for your production line. With my expertise in computer vision and machine learning, I can develop a robust solution that not only counts manufactured items but also detects operator presence reliably. My experience with models like YOLOv8 and TensorFlow ensures that the trained model will be tailored specifically to your needs, using images of your manufactured parts and typical shop-floor attire. I will implement an edge-ready solution that integrates seamlessly with your IP camera feed, providing real-time data streams for item counts and operator status. Additionally, I will create a user-friendly dashboard or API for live viewing and logging of counts, along with comprehensive setup documentation to empower your in-house team for future redeployments. I prioritize quality and robustness over speed, and I'm committed to delivering a high-performance solution that meets your specifications. I look forward to the opportunity to collaborate on this project.

@mahmod45

Hi, Your project is a great fit for a focused, reliable computer vision system. I can build an AI solution that accurately counts items in real time and detects operator presence with high stability. I’ll fine-tune a lightweight model (e.g., YOLOv8) on your specific parts and shop-floor conditions to ensure precise counting and minimal false detections. The system will run on your IP camera feed using OpenCV/GStreamer and output two clear streams: item counts and a boolean person-present flag. Deliverables include: Trained and fine-tuned model Edge-ready pipeline connected to your camera Real-time counting + presence detection Lightweight dashboard or API for live view and daily logs Clean codebase + documentation for easy redeployment (new cameras or SKUs) Brief walkthrough to hand everything over Since timeline is flexible, I’ll prioritize robustness—handling lighting changes, occlusions, and movement reliably. I can also recommend the best edge hardware setup if needed. Let’s build a system that works consistently in real production conditions. Best regards, Mahmoud

@khrystynad4

Hello, I can help build this real-time production-line vision system for item counting and operator-presence detection. This fits my background in Python, OpenCV, computer vision, real-time processing, debugging, validation, and performance-focused system design. I understand the goal is not generic surveillance, but a robust factory-focused solution that reliably counts manufactured parts and reports whether a person is present at the station. My approach would be: 1. Review your camera setup, line speed, product appearance, and operator view. 2. Prepare/train a detection model (YOLOv8 or similar) for your parts and operator class. 3. Build a real-time pipeline for IP camera input using OpenCV or GStreamer. 4. Add stable counting logic based on line crossing/tracking to avoid double counts. 5. Add person-present detection with a clear boolean status output. 6. Expose live counts/status through a lightweight dashboard or REST API. 7. Store logs for daily reporting and later analysis. 8. Provide setup documentation and a retraining/redeployment guide for new cameras or SKUs. I can structure the work in milestones: data review, model training, real-time inference, dashboard/API, and final deployment/testing.

@arvindk290

AI Vision System for Production Line Monitoring – Proposal I can deliver a robust, edge-ready computer vision system tailored specifically for your production environment, focusing on accurate item counting and reliable operator presence detection. Approach Use a fine-tuned YOLOv8 model for dual tasks: Object detection + tracking for precise piece counting Human detection for operator presence (with temporal smoothing to avoid false negatives) Implement a tracking-based counting logic (e.g., line-crossing with ID tracking) to ensure no double counts or misses. Optimize inference for edge devices (GPU/CPU) using OpenCV / GStreamer pipelines. Deliverables

@ZohaibSathio

Hi I can build a solid AI vision system for real time item counting and operator detection on your production line What I will deliver * Fine tuned YOLOv8 model trained on your parts and real shop floor conditions * Accurate counting using tracking so no double counting * Real time pipeline using OpenCV or GStreamer with your IP camera * Outputs item count and person present flag * Simple FastAPI dashboard or API for live view and daily logs * Edge ready setup for CPU or GPU * Clear docs so your team can retrain or add new SKUs easily Why me I have strong experience in computer vision and building real time AI systems and I focus on accuracy and reliability not just demos Quick questions Do you have sample videos What hardware will this run on What is the line speed Happy to share a quick system design before we start Zohaib

@tristanvdheever1

I recently completed a real-time production line monitoring system using YOLOv8, which included object counting and human presence detection for a manufacturing client. I specialize in deploying edge-ready AI vision solutions using OpenCV and TensorFlow, tailored to industrial environments. I can develop a finely tuned model for your parts and operators, deliver code compatible with IP camera feeds, and build a simple API or dashboard for live data and logs. Comprehensive setup documentation will ensure your team can redeploy as needed. Your emphasis on accuracy and robustness aligns perfectly with my approach. I would love to chat with you about your project, no pressure! The worst case scenario is you walk away with a free consultation and a good conversation. :) Kind Regards, Tristan Van Den Heever

@murtesa

Hello, I bring 3 years of experience in AI-driven solutions, particularly in computer vision and software architecture. I have a strong background in developing custom vision systems tailored to specific requirements. I understand your need for an AI camera system that can accurately count manufactured items and detect operator presence in real time. I am well-equipped to deliver a solution that includes a trained model fine-tuned on your specific data, edge-ready code for real-time processing, a user-friendly dashboard for monitoring, and comprehensive setup documentation for ease of deployment. Effective communication is key to project success, and I am committed to keeping you informed every step of the way. I am excited about the opportunity to work on this project and look forward to discussing it further with you. Best regards,

@leonardociobanu

I have reviewed your request, and I can provide a high-end, proprietary solution that already performs exactly what you need. My system is built on top-tier Made in Italy technology, specifically engineered for demanding industrial environments. This is a horizontal platform that goes far beyond simple counting: it covers Security, Safety, Maintenance, and Logistics in one single architecture. The beauty of this "Made in Italy" solution is its total customization: I can tailor the system perfectly to your specific production needs. It is a proven, rock-solid asset already trusted by global multinationals in the food, beverage, and logistics sectors to monitor complex assembly lines. However, I must be direct: your current budget is not sufficient. A professional-grade platform of this caliber, capable of providing the accuracy and multi-layer analysis you demand, requires a significantly higher investment. If you are looking for a versatile industrial powertool rather than a basic experiment, let's discuss how we can integrate my solution into your workflow. Please note: The bid amount I have entered does not reflect the final price of the solution. I have placed a placeholder bid because the platform's current budget range is significantly lower than the market value for an industrial-grade, Made in Italy system of this caliber. We can discuss a formal quote once we define the specific integration requirements for your production line.