A Deep Learning-Based Fire Detection System for Ships Using CNN-LSTM Networks

In the maritime field, major ship fire incidents cause significant property damage and seriously threaten crew members’ safety. Therefore, deploying early fire detection and warning systems is necessary to prevent fire spread in particularly harsh environmental conditions on ships. This paper proposes an intelligent fire detection model for shipboard applications using a deep learning architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). The CNN module extracts spatial features from image sensor data, combining the LSTM component with temporal patterns to enhance the ability to detect early fires. The training dataset is based on a 3D ship model in Unity, allowing for testing in real-world scenarios while ensuring safety. The experimental results show that the CNNLSTM model fully meets the requirements for fire hazard detection compared to conventional methods, increasing accuracy and fire safety on ships in the maritime environment.