Multi-Spectral Sensor Fusion with LiDAR Projection for Autonomous Fire Detection in Shipboard Environments

Abstract

As interest in autonomous ships continues to grow, the need for systems capable of automatically detecting and suppressing fires without human intervention is increasing. In this study, a four-channel + UV fire detection model integrating RGB, infrared (IR), and ultraviolet (UV) sensors, along with a LiDAR-based fire source localization system, is proposed. The fire detection model consists of an Xception-based network receiving four-channel (RGB + UV) images and a dense-based network processing UV time series data as inputs. The proposed model achieved high accuracy on the validation dataset, while the inclusion of UV data effectively eliminated false alarms caused by fire-like sources such as welding. For fire source localization, a four-channel YOLOv7 model combined with LiDAR projection was implemented, achieving a localization accuracy that significantly outperformed the conventional triangulation method. The effectiveness of the proposed system was validated through integrated experiments conducted under both open-space and occluded-fire scenarios.