CNN-based fire detection method on autonomous ships using composite channels composed of RGB and IR data

Abstract

Since fires grow exponentially after an outbreak, it is crucial to extinguish them quickly. In the case of fires occurring on commercial ships and naval vessels, smoke and heat in the narrow enclosed compartments of the ships make it difficult to extinguish fires, threatening human safety, and causing massive property damage. So-called autonomous ships are operated without crew members in charge of fire suppression. Therefore, it is crucial to improve the performance and reliability of the automatic fire detection and suppression system. However, the standard fire detection system installed on ships is not adequate for the level of fire risk. Existing fire detection methods still have false alarm problems, and it takes a long time for heat and smoke from the fire to reach the detector. In this study, the use of combined channel data (RGB-IR) is proposed as an image-based fire detection method applicable to ships. In nature, many animals obtain multiple wavelength information, which is advantageous for hunting or risk preparedness in a wild environment. In this way, it is assumed that both the characteristics of RGB data in the visible area and IR data in the infrared area may be utilized through the combined channel data. A fire detection model with composite channel data input was built using deep learning with a convolution neural network (CNN), a fire detection model using composite channel data input was constructed, and hyper-parameters were tuned during the training process to determine the optimal model and compare it with the existing RGB and IR models, respectively. Compared with the model using only conventional RGB or IR data, the fire detection accuracy of the model using RGB-IR combined channel data increased and the false detection rate decreased.