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I need a real-time, camera-based solution that can watch highway traffic and automatically flag three key offences: riders without helmets, motorcycles carrying three people, and any vehicle that exceeds the posted speed limit. The goal is to integrate the model with existing roadside or CCTV cameras so that the moment a violation is detected, the system captures the frame, tags the offence type and time-stamp, and stores it in a database or pushes it to an API that my team will expose. What I already have: several hours of highway footage in MP4 format, the speed-gun readings that correspond to those clips, and access to an NVIDIA GPU machine for training and inference. What I expect from you: • A trained, optimized computer-vision model (YOLOv8, RetinaNet, or another accurate ...

I need an Al-focused application that delivers 100% functionality in analysing and solving geometric patterns and sub patterns and sub to sub pattern at any level in jewellery. The primary objective is to ensure precision and reliable output. The features include: 1. Detecting patterns across different jewellery parts such as any parts of boundaries, textures, cuts, or design elements (including broad patterns, sub-patterns, and intricate sub-to-sub patterns). 2. Identifying low-quality or unclear components within these patterns and seamlessly replacing them with high-quality elements from other sources or jewels. 3. Ensuring replacements naturally blend into the overall piece, maintaining structural integrity and visual coherence. The focus is entirely on achieving accurate fun...

I have an existing GPU-accelerated network component that still compiles, but its inference logic is tangled and hard to maintain. I want the codebase cleaned up and modernised while keeping the same outputs and speed. Here is what I need from you: • Compile the current project on a recent CUDA-enabled Linux environment so we start from a reproducible build. • Re-architect the inference path, shifting it to clear, modular functions that follow TensorFlow best practices. The focus is code refactoring, not new features. • Preserve existing model weights and performance; any change that alters numerical results must be explained. • Deliver a git repo containing the refactored source, build scripts, and a short README that documents dependencies, compile flag...

NASA iMETRO Technical Support Specialist — Deep Space Autonomous Robotics Challenge Description NASA’s Integrated Mobile Evaluation Testbed for Robotics Operations (iMETRO) team at Johnson Space Center is running an open competition in which external teams develop autonomous robot software, first against a digital twin simulation and later on NASA hardware. The freelancer provides front-line technical support to competitors during Phases 1 and 2. Objective Enable solvers to successfully set up, run and troubleshoot the digital twin simulation environment; support the testing and running of solver solutions on NASA simulation hardware; and reduce engineering escalations through documentation and self-service guidance

I have a short segment of security-camera video showing two vehicles, recorded in completely dark conditions. The footage is low-clarity, so the license plates are currently unreadable. Your task is to isolate the key frames, enhance them, and recover each plate number. Please supply both the cleaned-up still images (full frame and a cropped close-up of each plate) and a simple text file with the characters you were able to decipher. The work has to stand up to basic forensic scrutiny, so I need a clear methodology, minimal artifacts, and proof that the digits you provide genuinely originate from the source video. You are free to use any combination of OpenCV, Topaz, AI super-resolution, de-blurring, or custom image processing—as long as the final output meets the goal: two ver...

I am looking for a skilled AI/ML and Data Science developer to help build or improve intelligent, data-driven applications and Python-based solutions. The project may involve: * Machine Learning and Deep Learning model development * Data Science, data cleaning, exploratory data analysis (EDA), and visualization * Predictive analytics, classification, and recommendation systems * NLP, chatbot development, and AI-powered customer support solutions * Generative AI application development and LLM-based solutions * Python development using FastAPI, Streamlit, and Gradio * Interactive dashboards, Plotly visualizations, and model performance evaluation * AI model integration, API development, and web application deployment I am particularly interested in practical projects with clear requireme...

Here’s a Freelancer project post focused on the bird-versus-drone problem: **Title: Improve Image-Only Drone Detection to Reduce Bird False Positives on Raspberry Pi + Hailo-10H** I have an existing Raspberry Pi camera pipeline running a single-class (“Drone”) YOLO model on a Hailo-10H. Birds, especially at a distance, may be detected as drones. I need a computer-vision engineer to develop and evaluate an image-only solution that reduces these false positives without losing too many real drone detections. The camera is fixed to the drone body. The current model is available as a compiled HEF; availability of its original training weights and dataset needs to be confirmed. Development must go in a new `DetectionV2` folder. Existing Detection, camera, telemetry, and inte...

I need a license-plate recognition engine that can reliably read Qatar, UAE, and Saudi Arabia plates when they appear in either still images or live video streams. High accuracy is non-negotiable—I am targeting production-level performance suitable for enforcement and analytics, so false positives must be minimal and confidence scores exposed. To succeed you will design, train, and package a full ANPR pipeline that: • detects the plate region in diverse lighting and angles, • recognises Arabic and Latin characters as well as the specific colour codes and icons used across the three countries, and • delivers results fast enough for real-time video (traffic-camera frame rates) while also accepting batch image inputs. Please outline your preferred tech stack (Open...

The project centers on building a production-ready TensorFlow 2.x model that classifies tabular data delivered to us through an internal API. I have the API specifications and sample payloads ready; you will turn those streams into a clean training pipeline, engineer the right features, and iterate until the classifier meets our performance targets in real-world tests. Scope of work • Data pipeline – pull the API data, handle preprocessing, and produce TensorFlow-friendly datasets for train/val/test splits. • Model development – design, train, and tune a deep learning architecture suitable for tabular inputs (e.g., wide & deep, Transformer, or other proven structures). • Optimization – experiment with hyperparameters, regularization, and callback...