Real-Time Rack Slot Detection

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Vision Tool for Real-Time Package Detection and Mapping ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and noticed you're looking for a vision tool to detect packages in industrial rack bays. You don't need to look any further; Zohaib is here to help you! My team has successfully completed over 50 projects in similar fields. I will utilize a one-stage detector like YOLOv5 or v7 to ensure the system operates below 100 ms per frame on an RTX-class GPU. ➡️ Why Me? I can easily build your vision tool as I have 5 years of experience in computer vision and machine learning. My skills include real-time object detection, model training, and Python programming. Additionally, I have a strong grip on video processing and machine learning frameworks, which will ensure that your project meets all the requirements. ➡️ Let's have a quick chat to discuss your project in detail. I can also show you samples of my previous work. Looking forward to our discussion! ➡️ Skills & Experience: ✅ Python Programming ✅ Object Detection ✅ YOLOv5 & YOLOv7 ✅ Model Training ✅ Video Processing ✅ Real-time Inference ✅ Machine Learning ✅ Data Annotation ✅ API Development ✅ Performance Optimization ✅ System Integration ✅ Documentation Waiting for your response! Best Regards, Zohaib

@irsolutions

With over 10 years of experience in web and mobile development, including expertise in real-time object detection, I understand the key challenge of building a vision tool for your project. Your requirement for detecting packages in an industrial setting with minimal latency aligns perfectly with my experience in delivering efficient and accurate solutions. In the realm of AI/ML development, I have successfully implemented real-time object detection projects, with a focus on optimizing performance and accuracy. Specifically, I have worked on projects in the industrial automation sector, where precise object detection is crucial for operational efficiency. I am confident in my ability to deliver a well-commented Python codebase for training and inference, along with trained weights ready for deployment that meet your performance targets. I will ensure a concise README is provided for easy setup and execution of real-time inference, culminating in a demo showcasing the system's accurate labeling in real time. I am eager to discuss further details and provide you with a timeline for training and fine-tuning the model. Please reach out to me to kickstart this exciting project together.

@AITSoft

Hello! I understand you need a fast and accurate tool to spot packages in specific slots of your fixed camera rack setup. I’ll start by using a good one-stage detector like YOLOv5 or YOLOv7 to quickly detect packages from your footage. Since your camera angle is constant and you have precise rack dimensions, I will map detections to the correct shelf index with minimal delay. I’ll provide clean Python code for training and live use, plus trained weights ready to deploy, and a simple README for setup and running. To prove it works, I’ll demo the system labeling placements in real time under 100 ms latency on an RTX GPU. I’m confident this will help you track packages fast and clearly. Could you clarify the exact format and labeling conventions of the slot ID scheme you’ll provide? What’s the average number and size range of packages we expect to detect per frame? Do you have any preference on the one-stage detector model or are you open to suggestions based on speed and accuracy? Is the input video always coming as pre-recorded MP4 files or will real-time camera streams be used as well? Would you like the demo as a recorded clip or prefer a live demo session for final acceptance? Thanks,

@Fazeennazar

Hello Greetings, After reviewing your project description, I am confident and excited to work on this project for you. a, I have some crucial points and questions to clarify. Please leave a message in the chat to discuss this, and I can share my recent work that is similar to your requirements. Thanks for your time! I am excited to hear from you soon. Best regards

@OutsourceMan

⭐⭐⭐⭐⭐ With my extensive experience in Python and Software Architecture, I'm confident that I'd be the perfect fit for your Real-Time Rack Slot Detection project. At CnELIndia, we've successfully completed numerous similar projects with utmost precision and speed. Our ability to deeply understand project requirements enables us to deliver effective solutions in minimal time while not compromising on quality. I understand that promptness is crucial for your project. I assure you that my team and I will deliver clean, well-commented Python code tailored specifically to your needs. Having worked with crisp-yet-complex video inputs like MP4 at 1080p, we've developed a knack for maintaining efficiency in detection processing. As expressed in your project description, meeting below 100 ms latency is a priority – rest assured that this target will be given utmost importance and strived towards relentlessly. To ensure absolute transparency and ease of usage, another integral aspect is the documentation. I will provide you with a concise README file detailing the environment setup, training steps, and how to run real-time inference. Additionally, you can count on us for necessary support post-project completion. Let's create a solution together that simplifies rack management effortlessly!

@softsolution2000

I have thoroughly reviewed the details of the Real-Time Rack Slot Detection project and believe my skills in Python, Matlab and Mathematica, Software Architecture, Machine Learning (ML), and C++ Programming are a perfect match. I am confident in developing a high-performing model that meets the latency target. Once we discuss the full project scope, we can adjust the budget accordingly. Please review my 15-year-old profile to see my extensive experience and commitment to client satisfaction. I am eager to begin working on this project and showcase my dedication. Looking forward to your response.

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you need a real-time vision pipeline that detects a package in fixed-camera 1080p rack footage and maps each placement event to the exact shelf slot index with very low latency. The main goal is accurate slot labeling in real time on an RTX-class GPU, with clean Python code, trained weights, and a deployment-ready inference flow. I’ve worked on similar computer-vision pipelines involving object detection, spatial mapping, and performance tuning, so I understand the balance between model accuracy, event logic, and inference speed. My approach would be to use a fast one-stage detector for package detection, calibrate the rack geometry from the fixed camera view, and add a lightweight placement-mapping layer that converts each detection into the correct shelf index while keeping the full pipeline under your latency target. I have a few questions to get a better understanding: Q1 – Will the sample footage include enough examples for each rack slot and different package sizes/lighting conditions for training and validation? Q2 – Do you want the system to detect only the final placed position, or also track the package motion until it settles into a slot? Q3 – Should the final inference pipeline process prerecorded MP4 only, or do you also want it structured for future live camera stream input? Looking forward to hearing from you.

@corpmember29

I’m building a vision tool that watches two single-row industrial rack bays and instantly tells us exactly which slot an operator uses when placing a package. The camera is fixed, so every frame shares the same perspective. Your task is to detect the package as soon as it appears and map that event to the correct shelf index with minimal latency. Key points you should know • Incoming video arrives as MP4 at 1080p. • Packages are the only class we care about. • Any recent one-stage detector is fine—feel free to start from YOLOv5, v7, or another performant model—as long as the finished pipeline stays below ~100 ms per frame on an RTX-class GPU. • I’ll supply sample footage, precise rack dimensions, and a simple ID scheme for every slot. Deliverables 1. Well-commented Python code for training and inference. 2. Trained weights ready for deployment. 3. A concise README detailing environment setup, training steps, and how to run real-time inference. 4. Demo proof—either a short clip or a live session—showing the system correctly labeling each new placement with the right rack coordinate. Acceptance The project is complete when the demo consistently outputs the correct rack index in real time on my provided test set while meeting the latency target. Let me know any questions and your estimated timeline to train and fine-tune the model. Skills Required

@Herculesz

Hi This project is not just object detection—the key technical challenge is turning each package appearance into a reliable, low-latency slot-mapping event without false triggers or coordinate drift. I can build this in Python using a fast one-stage detector such as YOLOv5/YOLOv7/YOLOv8, combined with a fixed-camera spatial mapping layer that converts each detected placement into the correct rack index. My experience includes computer vision pipelines, real-time inference optimization, GPU-based deployment, video event detection, and coordinate-based post-processing for industrial use cases. A common issue in setups like this is that detection alone is not enough, because the system also needs stable placement logic to know when a package is newly placed and which shelf slot it belongs to. I would handle that by combining detector output with calibrated rack geometry, slot-zone mapping, and event filtering so the system remains accurate and fast under real operating conditions. I can also provide well-commented training and inference code, trained weights, and a concise deployment README with real-time demo proof. The goal is a production-ready vision pipeline that correctly labels each placement event while staying under your latency target on RTX-class hardware. Thanks, Hercules

@seefattechnologi

I am highly appreciative to work on this specific task Real-Time Rack Slot Detection I can do my best. I am an Innovative PHP/Full stack developer having rich experience with so many successful Tasks. Let’s connect on chat for further discussion and start quickly. Thanks!!

@mubeenm5026

As the CEO of a tech company specializing in AI and automation, I am well-equipped to handle your real-time rack slot detection project. With our profound expertise in Python and C++, we can craft for you, from scratch and with impressive efficiency, a fully-functional vision tool using the YOLOv5 platform - guaranteed to meet stipulated performance thresholds. Having successfully completed numerous projects requiring advanced real-time object detection, such as this one, my team will derive accurate identification for packages in your industrial settings. As experienced software architects, we’ll deliver clean, well-commented Python code for training and inference as well as provide you with comprehensive documentation detailing every step in the process; ensuring that your system is easily maintainable and upgradable. Moreover, our strength lies not just in developing powerful solutions but also in offering outstanding post-project support. We’ll continue journeying with you even after the project is completed by maintaining clear communication, a transparent workflow, and providing you with future-ready solutions that can effortlessly adapt to your probable changes and growth.

@elishadiah326

Hi, this is a strong fit for my background because the task is very focused: detect one package class quickly, then map each placement to the correct rack slot in real time. Since the camera is fixed and the rack geometry is known, I’d approach this as a fast detection + spatial mapping pipeline rather than a generic vision project. I would fine-tune a lightweight one-stage detector such as YOLOv5/YOLOv7/YOLOv8, then add a simple post-processing layer that converts each detected package position into the correct shelf index using your rack dimensions and slot ID scheme. That keeps the system accurate while staying within the latency target on an RTX-class GPU. Deliverables would include: - well-commented Python code for training and inference - trained weights ready for deployment - concise README for setup, training, and real-time inference - demo proof showing correct rack labeling on your test set What gives me confidence here is my experience working on computer vision pipelines where model speed, spatial logic, and production usability all need to work together. Estimated timeline: 5 - 7 days for training, fine-tuning, slot-mapping logic, and demo validation. If you want a clean, fast vision pipeline built for real operational use, I’d be glad to help. Best regards, Diah

@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.

@juanjosec63

Hi, As a individual developer and I can jump into on your suitable time. I can help in your project (most important in this project libraries, modules, and relative issue during this project fix, improve, development) With my expertise in full-stack development and experience working with modern web technologies like Python, YOLO, OpenCV, PyTorch, real-time computer vision pipelines, GPU inference optimization, and slot-mapping logic for fixed-camera systems, i can build a fast and accurate detection pipeline that identifies each package placement and maps it to the correct rack index with deployment-ready training and inference code. You can expect clear communication, fast turnaround, and a high-quality result that fits seamlessly into your existing workflow. Best regards, Juan

@Ekarthaan

Hi Natalia P., Just last week I completed a similar task successfully, so I can get started on this without any ramp-up time. Two questions: 1) Will you provide per-slot pixel polygons from the fixed view, or should I derive them via a one-time homography from your rack dimensions? Please confirm slot count per bay and acceptable overhang tolerance. 2) What is the exact “placement” trigger—first intersection with a slot ROI or when motion settles? Should we suppress duplicates while the operator’s hand occludes and report only the final settled slot? Suggestions: 1) Use a lightweight YOLO (v8n/s or v7-tiny) compiled with TensorRT FP16 (INT8 if accuracy holds), plus GPU video decode and per-bay ROI/motion gating to keep end-to-end latency <100 ms. 2) Add track-by-detect (OC-SORT/BYTETrack) with a per-slot finite-state machine and short debounce to avoid double counts and ensure correct indexing under brief occlusions. Action Plan: Phase 1: Ingest sample video, calibrate homography, define slot polygons/index map, benchmark decode path. Phase 2: Fine-tune detector on your clips with targeted augmentations; export ONNX/TensorRT. Phase 3: Build real-time pipeline (GStreamer/FFmpeg→CUDA), ROI gating, tracker, event logic; async queues; measure latency. Phase 4: Validate on your test set, tune thresholds, deliver code/weights/README, and provide demo. Estimated timeline: 5–7 days after data receipt. Best Regards, Sid

@abhisaini0188

Hello, As a Full Stack Developer with over 8 years of experience, I've dedicated my career to driving results through AI-powered applications and machine learning integrations that can serve your project well. My extensive expertise in Computer Vision, Deep Learning, and Machine Learning, especially YOLO, aligns seamlessly with your project requirements. I'm confident my skills will help you deliver the real-time rack slot detection system you need. Not only am I experienced in implementing one-stage detectors like YOLOv5, but I also have a deep understanding of deploying and fine-tuning models for real-time inference within tight latency constraints using GPUs common to your setup (RTX-class). This way, I can assure your project will meet its objective at minimal latency levels. In fact, the 1st place standing for the M5 Forecasting challenge speaks volumes about my proficiency in data processing and deploying resource-efficient ML solutions. I can not only provide the requisite trained weights but also ensure comprehensive documentation on environment setup, training steps, and running real-time inference. Additionally, leveraging my backend expertise in Python, FastAPI or Flask, I can optimize storage and retrieval of the rack dimension dataset for faster processing. You'll find my code efficient for further development and my approach business-focused. With Regards!

@FaizalShaik80

Hi, hope you are well. I’ve carefully reviewed your requirements, and this is essentially the same type of project I completed two months ago. I am a skilled freelancer with 6+ years of experience in Python, C++ Programming and I can deliver the results as quickly as possible. Feel free to visit my profile to check latest work and feedback from clients. Connect in chat to discuss details and next steps. Regards.

@hotanloc

Hello, I have already completed a similar project successfully. Project review: https://www.freelancer.com/projects/python/people-detection-counting/reviews Please take a look at my previous Freelancer projects and reviews. Your project aligns well with my experience, and I will do my best to meet all your requirements. My core expertise is in object detection, tracking, and counting. I have developed many computer vision projects, including: * People detection and counting * Product detection and counting * Defect detection in manufacturing * Vehicle detection and speed analysis I have strong experience in image processing and CCTV video analysis, where objects are detected and counted from images or video streams. For your project, I will: 1. Train a model using annotated data to generate optimized weights 2. Develop the detection system based on the trained model 3. Analyze detected objects and display the results clearly My technical stack includes YOLO, OpenCV, TensorFlow, PyTorch, Keras, OCR, and other ML/DL frameworks. With my experience in machine learning and deep learning, I can build an accurate detection system and implement post-processing (such as object counting and analysis) using OpenCV. I am confident I can deliver a high-quality solution within a short timeframe. Please feel free to send me a message so we can discuss your project in more detail. I look forward to hearing from you. Thank you.

@OscarMozo

Hi, I can help you with this. I am a developer with extensive experience with automations and integrations. I've helped clients with similar projects. Let me know your interest, Sincerely, Nicolas