YOLO Computer Vision Engineer for Real-Time Vehicle & License Plate Recognition (ANPR)

Project Overview: We are looking for an experienced Computer Vision and Machine Learning Engineer to develop a real-time Automated Number Plate Recognition (ANPR) system using YOLO, OCR, and Python. The system should detect vehicles, recognize and extract license plate numbers, and track vehicles across video streams. The solution will be designed for real-world applications such as parking facilities, vehicle entry/exit gates, and traffic monitoring. We need a reliable solution that can process multiple camera streams simultaneously and provide structured vehicle recognition data through a centralized monitoring system. Project Duration: 1 Month (4 Weeks): The selected developer must be able to deliver a functional, tested, and documented ANPR application within the agreed timeline. About the Project: The objective is to build an intelligent vehicle monitoring system that automatically detects vehicles, recognizes their license plates, and maintains vehicle activity records. The system should handle different environmental conditions, including changing lighting, vehicle movement, different camera angles, and partially visible number plates. The primary focus is to develop a working end-to-end solution with accurate vehicle detection, license plate recognition, tracking, and database integration. Scope of Work: 1. Vehicle & Number Plate Detection: - Review existing camera configurations and sample video footage. - Implement a vehicle detection model using YOLOv8, YOLOv11, or newer versions. - Detect different vehicle categories, including cars, motorcycles, trucks, buses, and vans. - Detect and localize license plates accurately. - Fine-tune the detection model using available project-specific datasets, if required. 2. License Plate Recognition (OCR): - Integrate an OCR engine to extract text from detected license plates. - Evaluate suitable OCR solutions such as EasyOCR, PaddleOCR, or custom recognition models. - Handle different license plate formats and character variations. - Apply image preprocessing techniques to improve OCR performance. - Handle blurry, tilted, and partially visible license plates where feasible. - Validate extracted plate numbers and minimize incorrect recognition. 3. Vehicle Tracking: - Implement multi-object tracking using ByteTrack, BoT-SORT, DeepSORT, or similar algorithms. - Assign unique tracking IDs to detected vehicles. - Maintain vehicle tracking information across video frames. - Associate recognized license plate numbers with corresponding vehicle IDs. - Prevent duplicate vehicle records during continuous tracking. 4. Real-Time Camera Integration: - Integrate multiple IP camera streams using RTSP. - Process multiple camera feeds simultaneously. - Implement efficient frame processing and video streaming. - Handle camera disconnections and automatic reconnection. - Optimize the pipeline for stable real-time performance. 5. Recognition Data & Event Management: The system should capture and store: - Recognized vehicle registration numbers. - Vehicle category and tracking ID. - Date and time of detection. - Camera ID and detection location. - Captured vehicle and number plate images. - Vehicle entry and exit events, where applicable. 6. Backend & Database Integration: - Develop backend APIs using Python and FastAPI or Flask. - Integrate PostgreSQL, MySQL, or another suitable database. - Store vehicle detection records and recognition results. - Provide APIs to retrieve, search, and filter vehicle records. 7. Testing & Performance Optimization: - Test the complete system using real-world video footage. - Evaluate vehicle detection and OCR recognition accuracy. - Measure processing speed (FPS) and system latency. - Optimize GPU utilization and processing performance. - Fix critical bugs before final deployment. ### Project Timeline – 4 Weeks The project must follow the following development milestones: Week 1: Research, Setup & Detection Development - Review camera footage and project requirements. - Select the appropriate YOLO model and OCR approach. - Configure the Python development environment. - Implement vehicle and number plate detection. - Complete the initial detection prototype. Expected Milestone: Working vehicle and license plate detection prototype. Week 2: OCR & Vehicle Tracking: - Integrate OCR for number plate recognition. - Implement image preprocessing and text validation. - Integrate ByteTrack, BoT-SORT, or a suitable tracking algorithm. - Associate recognized number plates with vehicle tracking IDs. - Test detection and recognition using sample videos. Expected Milestone: Working ANPR pipeline with vehicle detection, OCR, and tracking. Week 3: Camera Integration & Backend - Integrate multiple RTSP camera streams. - Implement simultaneous video processing. - Develop backend APIs. - Integrate database storage. - Implement vehicle recognition logs and event management. - Optimize real-time processing performance. Expected Milestone: End-to-end backend and multi-camera ANPR system. Week 4: Testing, Optimization & Final Delivery - Perform end-to-end testing. - Improve detection and OCR performance. - Fix bugs and optimize processing speed. - Complete deployment configuration. - Prepare source code and technical documentation. - Conduct final demonstration and handover. Expected Milestone: Fully functional, tested, and deployable ANPR application. Expected Deliverables: 1. YOLO-based vehicle and number plate detection model. 2. OCR-based license plate recognition module. 3. Multi-object vehicle tracking implementation. 4. Real-time processing pipeline for multiple IP camera streams. 5. Backend APIs for vehicle recognition and record management. 6. Database integration for storing detection records. 7. Complete source code with proper documentation. 8. Model evaluation results, including precision, recall, mAP, OCR accuracy, and FPS. 9. Installation, configuration, and deployment guide. 10. Final working demonstration and handover session. *Required Skills & Experience* * Strong experience with Computer Vision and Machine Learning. * Hands-on experience with YOLOv8, YOLOv11, or newer models. * Experience building Automated Number Plate Recognition (ANPR/ALPR) systems. * Strong Python, PyTorch, and OpenCV skills. * Practical experience with OCR technologies such as EasyOCR or PaddleOCR. * Knowledge of multi-object tracking algorithms such as ByteTrack, DeepSORT, or BoT-SORT. * Experience with image preprocessing and model fine-tuning. * Experience developing REST APIs using FastAPI or Flask. * Knowledge of GPU-based inference and performance optimization. Nice to Have: - Previous experience with parking management or traffic monitoring systems. - Experience with real-time vehicle tracking and analytics. - Knowledge of international license plate formats (US, UK, EU, etc.). - Experience with nighttime vehicle and number plate recognition. - Experience with NVIDIA CUDA, ONNX, or TensorRT. - Experience with React, FastAPI, PostgreSQL, or MySQL. How to Apply: When submitting your proposal, please include: 1. Examples of previous YOLO, OCR, or ANPR projects you have developed. 2. Links to GitHub repositories, live demos, or demonstration videos. We prioritize developers with practical experience building and deploying real-world Computer Vision applications rather than candidates with only academic projects or notebook-based implementations. Important : This is a fixed 1 month development project. We expect weekly progress updates, milestone-based delivery, regular testing, and complete source code handover.