Object Detection and Classification for Autonomous Surface Vehicles (ASVs) Through Near-Infrared Imaging

This study proposes a novel pipeline that starts with NIR video data to advance the field of near-infrared (NIR) object detection. NIR video sequences must be split into individual frames to analyze the environment accurately. These frames are then produced and structured for the cutting-edge object identification model YOLOv8. The formatted NIR frames are then used to train the YOLOv8 model, enabling reliable and effective object recognition in complicated maritime settings. Unmanned surface vehicles (USVs) and autonomous maritime systems have a wide range of potential applications, and the integration of NIR video data and YOLOv8 promises improved object detection capabilities, particularly in low-light and challenging settings. Combining NIR imagery, frame conversion, and YOLOv8 training can dramatically enhance USVs' navigational capabilities and make them more effective in maritime conditions. This study aims to increase autonomy and safety in maritime applications, from surveillance and environmental monitoring to logistics and search and rescue operations.