YOLO Jobs

Project Title: Iraqi License Plate Detection and Recognition using Python (YOLO + OCR) Description: I am looking for a developer to build a complete Python project for automatic Iraqi vehicle license plate detection and recognition. Project Requirements: 1. Detect Iraqi license plates from vehicle images using a deep learning model (preferably YOLOv8). 2. Crop the detected license plate from the image. 3. Apply OCR to recognize the license plate number and characters. 4. The system should support Arabic letters, English letters, and Arabic-Indic numbers used in Iraqi license plates. 5. Use data augmentation techniques (rotation, blur, illumination changes) to improve model performance. 6. Provide training and testing scripts. 7. Provide the full Python source code. Tools preferr...

I need a YOLOv8 model trained to detect and classify approximately 12 types of fishing vessels from provided images. The dataset will be in YOLO/COCO format. Requirements: - Target accuracy: ~90%+ - Input image resolution: 1280x1280 - Performance metrics: Precision, Recall, F1 Score - Inference optimization: Balanced between speed and accuracy Deliverables: - Trained weights (.pt) - Training script - Inference code Ideal Skills & Experience: - Expertise in YOLOv8 and PyTorch - Experience with object detection and model training - Familiarity with performance metrics and optimization techniques

I’m building an MVP that inspects coal quality from both photos and short videos. The system must return a simple breakdown—e.g., “Coal 75 % / Stone 25 %”—and lay the groundwork for optional size-distribution analysis in later releases. What you’ll have to work with • A batch of truck-loading videos and corresponding lab quality reports. Phased scope Phase 1 – Extract still frames from the supplied videos and organise them into a clean, labelled image set. Phase 2 – Train a computer-vision model (YOLO or a comparable CNN in Python) able to distinguish coal from stone. Baseline accuracy is sufficient for now; we can iterate later. Both real-time and batch inference modes should be supported. Phase 3 – Wrap the model in...

this project is to develop an infrared small target detection system using classical image processing techniques. The system will analyse infrared images and detect very small targets that are often hidden in complex background environments such as sky, sea, and land scenes. The project focuses on combining local contrast-based methods and nonlocal spatial information techniques to improve detection accuracy while maintaining computational efficiency. The project will use a public benchmark dataset: Dataset: NUAA-SIRST Dataset Source: GitHub open-access repository Dataset Link: Step 1 – Data Preparation Step 2 – Image Preprocessing Step 3 – Local Target Enhancement Implement the Dual Window Local Contrast Method (DW-LCM). Step 4 – Nonlocal Background Suppressi...