AI-Powered Load Counting App

@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

@mohjib092

Hello, I can help build a real-time computer vision system for your existing IP cameras that detects and counts Bags, Boxes, and Tins from RTSP/HTTP streams, including direction tracking, double-bag recognition, confidence alerts, CSV export, and a live dashboard. The solution can be developed using YOLO/OpenCV with a Windows-ready deployment package, source code, model weights, and setup documentation. Please share sample camera footage and current camera specifications so I can review detection conditions and confirm the best approach, timeline, and hardware requirements.

@quan9824

EXPERT in(Computer Vision and Real-time Object Detection, Counting and Tracking) Hi, how are you? I checked your detail carefully. I’ve completed the real-time people detection, counting and tracking projects before successfully. Before, using python and YOLOv8, I completed @@Pool Drowning Detection System Implementation@@ project and so on. You can check my works history on my portfolio. I am sure this field and I will do my best. I always thought "It is your job, it is also my job". Awarding me will be the fastest way to complete your task with the best rates possible. THANK YOU.

@rupa37

This is Ambitious but is doable as a lot of open source python solutions are there which we can levrage . Please message me to Discuss hoping to hear from you thank you -- looking forward

@iimtpradeepdash

Hi! The point about handling people carrying two bags at once is the tricky bit—most counting apps miss those edge cases and lose accuracy fast. Distinguishing single-bag versus double-bag events, alongside mixed sizes for boxes and tins, does need more than a templated model. I built an AI logistics platform last year (PackSmart AI—live model demo available) where real-time CV had to cope with mixed objects, occlusion, and live feed issues. For you, I’d use a YOLO-based model tight to your camera angles, with an RTSP puller and a small Windows app that logs, checks confidence, raises alerts, and gives you a dashboard for export. Most risk is in the detection logic for overlapping items and direction—happy to include an interim demo for live adjustment. One question—what camera heights and angles do you have at the bay? That can really affect model accuracy out of the gate. I can send you a free 1-page build and test outline for this, no strings. You can also see similar projects at work.techindika.com. — Pradeep

@souvikbasu

Hi, I'm a Python developer with hands-on experience in computer vision and real-time video processing — this is a good fit for what I've built before. The trickiest part here is the double-bag detection. Standard YOLO object detection tends to merge two bags held close together into one bounding box. I'd handle that with YOLOv8 instance segmentation plus a custom size heuristic that flags oversized detections as double-carry events. Overall approach: - Video ingestion: OpenCV reading your RTSP/HTTP stream with auto-reconnect on camera drop - Detection: YOLOv8 fine-tuned on your bag/box/tin categories, runs on CPU (GPU-accelerated if available) - Counting + direction: Centroid tracker across a virtual line at the bay threshold — onto vs off the lorry - Dashboard: Lightweight Flask UI with live counts and per-lorry CSV export - Logging: SQLite/PostgreSQL with timestamp, category, direction, and session per lorry Happy to jump on a call to discuss the setup.

@Shrutibimpl

Hello There, As per my understanding you want an automated cargo counting system for your loading bay that tracks bags, boxes, and tins via your existing IP cameras. I have hands on experience building high precision computer vision pipelines for logistics and warehouse automation using real time object detection and tracking. I will give you an extra set of eyes that never gets tired or misses a count, ensuring your inventory logs match exactly what moves onto your trucks. You will get a clear dashboard that alerts you the moment something is unclear, giving you total transparency over your loading operations without needing to manually watch hours of footage. This system is designed to handle the messy reality of a busy warehouse, distinguishing between single and double items so your data remains accurate even during peak hours. I will build the solution using Python and YOLOv8 for high speed object detection, specifically trained on your three item categories to handle varying sizes and overlapping items. I will implement a DeepSORT tracking algorithm to determine movement direction and prevent double counting as items pass through the camera field. The backend will process RTSP streams via OpenCV and store every event in a local SQLite database, serving an interactive web dashboard built with Flask that supports CSV exports and real time connection monitoring alerts. Best regards, Bharat Joshi

@SmartCodeDev

Hello, I can build a real-time computer vision system that connects to your IP camera feeds and automatically detects and counts Bags, Boxes, and Tins as they move onto or off trucks. I will use a YOLO based model with tracking logic to handle different sizes and cases like single or double-bag carrying while ensuring accurate direction based counting. All events will be logged with timestamps and shown on a simple web dashboard with CSV export for reporting. The system will include confidence monitoring and alerts for camera issues or low accuracy situations. It will be delivered as a Windows-ready package with installer, model weights, source code, and setup guide, plus a demo to validate performance before final delivery.

@mahads31

As a versatile web and software developer, I don’t just provide code, but also solutions that reliably and scalably meet unique client needs. Combining this mentality with my deep knowledge of areas like custom software and system architecture, automation, and smart workflows, I'm confident in my ability to build an intuitive AI-powered load counting app that meets your specific requirements. I have extensive experience with real-time video processing frameworks like OpenCV, TensorFlow, and YOLO - the very technologies you need for this project. I can leverage these tools' capabilities to create a robust computer-vision powered model capable of recognizing and accurately counting items live from your IP camera feeds, while maintaining high reliability and efficiency - even across different dimensions! On top of that, my capacity to turn complex requirements into clean and impactful solutions guarantees an easy-to-use dashboard for viewing log data as well as effortless CSV exports. And to ensure constant monitoring without a hitch, trust me to integrate a fail-safe alert mechanism when connections drop or confidence hits low.

@TeamGTW

Hello! Based on your project description, you are looking to build an AI powered load counting application that connects to your existing IP camera feeds and accurately tracks items moving onto and off lorries in real time. The system will identify and count Bags, Boxes, and Tins while handling varying object sizes and scenarios such as people carrying two bags simultaneously. I will focus on delivering a reliable computer vision solution using technologies such as YOLO, OpenCV, and TensorFlow to process RTSP or HTTP camera streams in real time. The system will detect object categories, determine movement direction, and maintain accurate counts with timestamp based logging. Special attention will be given to handling different box and tin dimensions along with distinguishing single and double bag events for precise tracking. I will also develop a lightweight web dashboard where counts, logs, alerts, and CSV exports can be managed easily. The application will include camera disconnection alerts and low confidence detection warnings to support manual verification when needed. I specialize in AI based computer vision and real time monitoring systems with 7 plus years experience and I have done similar work in past please open the chat window so I can share with you. Thank you for considering my proposal. I look forward to hearing from you soon. Best regards, Nikita Gupta

@Swapnil2387

Hello, This is a strong computer-vision use case, and I can build a reliable system to detect, classify, and count items from your IP camera feeds in real time. Approach: I’ll use a YOLO-based detection model (fine-tuned for Bags, Boxes, and Tins) combined with tracking logic to ensure accurate counting and direction detection (in/out of lorry). Core system: • RTSP/HTTP stream ingestion from your cameras • Object detection (Bags, Boxes, Tins with size variations) • Logic to distinguish single vs double bag events • Object tracking (to avoid double counting) • Direction detection (entry/exit using virtual line crossing) Dashboard & alerts: • Live dashboard showing counts with timestamps • CSV export for reports • Alerts for camera disconnect or low-confidence detection Technical stack: • Python + OpenCV + YOLO (Ultralytics) • Lightweight web dashboard (Flask/React) • Runs locally on Windows PC What you’ll get: • Complete working system + source code • Trained model weights • Installer/setup guide • One interim demo with live footage validation • Final tuning for accuracy I’ll focus on accuracy, stability, and real-world reliability so counts remain consistent even with variable sizes and multiple items. Ready to discuss sample footage and get started. Best regards

@kratika0510

When it comes to your AI-powered load counting app, my skills in PHP and my familiarity with powerful models such as OpenCV, TensorFlow, and YOLO position me to be an excellent fit. I understand the need for an accurately tracking system that dynamically handles different dimensions and various categories, and I'm confident in my ability to deliver just that.

@fizaa170

With my extensive experience in developing SaaS platforms and AI-powered systems, I'm more than equipped to handle your AI-Powered Load Counting App project. As a full-stack engineer and automation specialist, I don't just stack technologies; I design complete, scalable systems. I understand your need for a precise and reliable solution that can handle real-time data, and that's exactly what I do - build systems that run businesses. I am well-versed in Computer Vision models such as OpenCV, TensorFlow, YOLO etc, which would be essential for your project in detecting the different categories of items (Bags, Boxes and Tins) as well as distinguishing single-bag from double-bag events. I can deliver the software package you need to run it on your Windows PC with all the necessary components – installers, model weights, source code and setup documentation. Carrying my doses of patience and keen eye for detail into your project, I’d ensure consistent accuracy of detection by conducting interim demos to confirm against live-footage. What sets me apart is not merely stacking technologies but creating strategic solutions that have quantifiable business impacts. My clients have benefited from faster MVP launches, saved hours through automation, improved scalability and growth-oriented designs. Clear communication without delays is another area where I excel; you'll always remain informed at every stage of development. Let's transform your vision into a robust and efficient system!

@Ankurhardia34

Hello, I’m Ankur Hardiya, a friendly freelance developer with an awesome team. I read your requirement for a powered load counting app and I’m super excited to develop and design a fantastic Android and iOS application for you. With my experience in Flutter and native languages, I can build high-performing apps for both Android and iOS platforms. Whether you need an eCommerce app, a custom business app, or anything in between, I can deliver: * Native app experience for optimal performance * User-friendly interface and intuitive navigation * Seamless integration with backend systems * Ongoing maintenance and updates I’m passionate about creating mobile apps that make a difference, and I’m eager to discuss your project in detail. Thanks a bunch for thinking of me for your project. I’m all set to turn your ideas into something amazing in today’s competitive world. Regards, Ankur Hardiya

@Royalitservices

As an experienced Full-Stack Digital Developer and Creative Technologist, I possess the diverse skill set required to excel in your project. My core Android and PHP expertise will assist in pulling the live feeds from your IP cameras and creating a computer-vision model capable of recognizing multiple item types, distinguishing single-bag from double-bag events, and even working with differing dimensions of boxes and tins. I can also efficiently log the count data alongside timestamps, present it on a user-friendly web dashboard, and export it as CSV, just as you need. To ensure we stay on track with the project timeline and accuracy goals, I commit to regular interim demos so that we can confirm the detection accuracy against live footage at different stages. Furthermore, packaging everything you need—including installers, model weights, source code, and brief setup documentation—is something I consider part of a complete delivery. By hiring me for this task, you're gaining a dedicated professional who's known for clean, scalable and performance-driven development. Let's transform your existing IP cameras into a powerful AI Load Counting App that not only meets but exceeds all your expectations!

@enricos0

Hi Client, At NousTech, we specialize in building tailored software solutions that integrate seamlessly with your existing infrastructure to enhance operational efficiency and accuracy. We propose developing a custom computer vision application that connects to your IP cameras via RTSP/HTTP streams, utilizing advanced models like TensorFlow or YOLO to accurately detect and classify Bags (including distinguishing single vs. double), Boxes, and Tins of varying sizes. The system will reliably count items moving onto and off each lorry, logging time-stamped data with directionality. You will access real-time counts through an intuitive web dashboard with CSV export functionality. Our solution includes robust connection monitoring and confidence-level checks that trigger immediate on-screen alerts for manual verification. Delivered as a complete Windows package—installer, model weights, source code, and setup documentation—we guarantee on-time delivery within one month and at least one interim demo to validate detection accuracy using your live footage. Let’s transform your loading bay operations with smart, data-driven insights. Best regards, Adam Faustino NousTech

@shahidhabib791

Hello, I can develop a real-time computer vision system for your loading bay that accurately detects and counts Bags, Boxes, and Tins from your existing IP camera feeds. The solution will connect to your RTSP/HTTP streams and use a trained object detection model (YOLO/OpenCV/TensorFlow) to identify items, track movement direction (on/off lorry), and maintain a live, accurate tally even when multiple items are carried together, including single vs double-bag handling and variable object sizes. The system will be built with robust tracking logic to ensure reliable counting across different lighting and movement conditions, with confidence thresholds to reduce false detections. All counts will be logged with timestamps and displayed on a simple web-based dashboard, with CSV export functionality for reporting and analysis. If camera feed drops or detection confidence falls below safe limits, the system will generate real-time alerts for manual verification. I will package everything for Windows deployment, including source code, model weights, setup instructions, and a ready-to-run installer. You will also receive an interim working demo to validate accuracy on live footage before final delivery. best regards Habib Ullah

@vimals87

Hello, I understand you need a computer-vision solution that can use your existing IP camera feeds to count Bags, Boxes, and Tins moving on and off each lorry in real time. The goal is to deliver an accurate Windows-based system with live detection, direction tracking, logs, alerts, dashboard, and CSV export. Here’s what I can provide: * RTSP/HTTP camera stream integration with OpenCV/YOLO-based item detection for bags, boxes, tins, and size variations * Direction-based counting logic for loading/unloading, including single-bag and double-bag event handling * Web dashboard with timestamped logs, CSV export, confidence alerts, camera disconnect alerts, source code, installer, and setup documentation I bring over 10+ years of experience in software development, AI/ML integrations, computer vision workflows, dashboards, automation systems, and scalable application development. I can also support an interim demo using sample/live footage to validate detection accuracy before final delivery. Just to clarify a few things: * Do you already have recorded footage for model training and testing? * How many IP cameras need to be connected in the first version? Please come to the chat box to discuss more about your project. Best regards Vimal

@MaazDeveloperDot

With my keen focus on software architecture and holistic approach to web development, I'm excited to take on your unique project of building an AI-powered load counting app. My experience in creating clean, modern, and high-performing applications will prove invaluable in delivering a reliable and efficient solution for your loading bay. I've previously worked with OpenCV, TensorFlow, and YOLO, making me well-versed in the technologies needed for implementing a computer-vision model for object detection. Your vision aligns perfectly with my skill set: pulling live feeds from IP cameras, identifying objects accurately and quantity in real-time, logging with timestamps, and providing a web dashboard for visibility is all within my expertise. Moreover, I understand the importance of maintaining connection and ensuring data integrity; hence, implementing an automated alert system for manual checking when there's loss or low confidence in count will be part of my strategy. To ensure that the solution fits seamlessly with your existing setup and aligns with your business needs, I'll deliver everything you require—installers, model weights, source code—alongside clear setup documentation. You can expect regular demos throughout the project timeline to confirm the accuracy against live footage.