LAUSR

With the rapid development of transportation infrastructure and the growing water transportation industry, many bridges have been constructed across navigable waters. However, collisions between ships and bridges remain a frequent occurrence, particularly under low illumination conditions. To address this issue, this paper proposes a full-time bridge-ship collision alert approach based on the combination of the visible spectrum and infrared cameras, which includes: (1) Capturing and identifying ships in the channel using a single visible spectrum camera is challenging in low illumination environments such as nighttime. To overcome this limitation, we propose the use of infrared thermal imaging technology, which can monitor ships at night due to their inherent emissivity of ship thermal radiation. By comprehensively analyzing factors such as the real-time lighting environment and comparing the advantages of visible spectrum cameras and thermal infrared cameras in object recognition under various illuminance levels, we determine the effectiveness of the decision-making method of the dual-channel camera for ships under different illumination conditions. (2) To detect and track ships in the channel, a deep learning-based automatic detection and tracking method is designed using visible spectrum and infrared thermal imaging cameras, providing early warning of dangerous trajectories of ships that threaten the piers. To address the real-time identification and positioning of ships by dual-channel cameras in different light environments, we adopt a target detection network and a displacement calculation algorithm for multi-target tracking. Analysis results demonstrate that this method can automatically assign visible spectrum and thermal imaging cameras as effective input sources in different illumination environments to detect and warn ships with dangerous trajectories, contributing to the safety of navigable ships and bridges in low illumination conditions.