Edge Computer Vision & Driver Assistance System

Core+ is seeking a skilled freelancer to develop an Edge Computer Vision & Driver Assistance System for industrial machinery, specifically for railways. The project involves utilizing NVIDIA Jetson and YOLO for edge computing and object detection. The ideal candidate / team or agency will have experience in computer vision and driver assistance systems, with a strong understanding of industrial machinery applications. Our current belief is we need the following skills: 1. Edge Computer Vision / Machine Learning (ML) Engineer 2. Embedded Systems / Edge AI Software Engineer 3. HMI (Human-Machine Interface) Developer 4. Backend / Systems Engineer (GPS Focus) Our project will have 4 core areas: Scope & Modular Architecture: The project must be quoted using a modular pricing structure as it may be deployed independently: Module A: Driver Machine Interface (DMI) & Forward-Facing Smart Camera (identifying track obstructions, incorrectly set railway points, generating real-time alarms). Module B: Operator Machine Interface (OMI) & Asset Protection (utilizing GPS data overlays, distance countdowns, and ETA to trackside assets). Module C (Optional/Enhancement): Tamping Workhead Smart Camera with an 8-zone "Safe to Tamp" color-coded visual layout. Module D: Shared Infrastructure, localized edge backend, and event logging. We are seeking competitive proposals for the design, architecture, and development of a Machine Learnt, AI-enabled Smart Camera & Operator Assistance System deployed on moving heavy machinery (On-Track Machines). The system will provide real-time obstruction detection, asset protection, and operator guidance via low-latency industrial display feeds. This is an entirely edge-based application. The final software must run locally on physical hardware subjected to extreme railway vibrations, high weatherproofing standards, and low-light operations. Hardware selection guidance (Industrial AI cameras, edge GPUs, and touchscreen monitors) is part of the deliverables. Submission Requirements: Please provide a response identifying standalone pricing per module, combined pricing, software licensing expectations, and a phased timeline across standard architecture, AI model training, and testing phases. NOTE: To accelerate Phase 1 and maximize efficiency, we will provide the winning partner with a dataset of pre-recorded front-facing rail video footage. The initial phase of development will focus strictly on training and validating the object detection models using this data before moving to on-machine hardware integration.