V2IR: Visible-to-Infrared Image and Video Translation Using Pix2Pix Generative Adversarial Network with Integrated YOLOv8 Object Detection

Infrared imaging plays an indispensable role in night-vision surveillance, autonomous navigation, search-and-rescue operations, and defence systems, yet the hardware infrastructure required to capture genuine thermal data remains prohibitively expensive for large-scale deployment. This paper introduces V2IR, a complete end-to-end pipeline that synthesises perceptually convincing infrared representations from ordinary RGB photographs and video streams by means of a Pix2Pix Conditional Generative Adversarial Network. The generator adopts a U-Net 256 architecture with eight levels of skip-connected encoder–decoder blocks trained over 200 epochs on the Large-scale Visible-Infrared Paired (LLVIP) dataset comprising 15,488 co-registered image pairs. A linear learning-rate decay schedule, Adam optimisation with β1 = 0.5, and a composite loss combining vanilla GAN cross-entropy with an L1 reconstruction term weighted at λ = 100 are employed. The synthesised infrared output is subsequently fed into a YOLOv8 anchor-free detector hosted on the Roboflow Serverless API and fine-tuned on FLIR thermal imagery, achieving reliable person and vehicle localisation under conditions where RGB-domain detectors degrade severely. The complete system is delivered as a Flask web application that supports single-image inference, URL-based input, and frame-level video processing with real-time progress streaming via Server-Sent Events. Qualitative and quantitative analysis across different training epochs demonstrates progressive improvement in structural fidelity, thermal contrast, and detection confidence, validating V2IR as a cost-effective software-based alternative to dedicated infrared acquisition hardware.