Experimental results proved that the proposed method can be used successfully for the protection of ships and in monitoring fires in ship port areas. Advancements in deep learning models hold the potential to enhance safety measures, and our proposed model in this paper exhibits this potential. However, our model can be utilized as a real-time fire detector in challenging environments and for small-object detection. Objects having visual similarities to fire may lead to false prediction and detection by the model, but this can be controlled by expanding the dataset. The proposed strategy successfully achieves an accuracy of 93% in detecting fires to minimize catastrophic incidents. Our model, through rigorous evaluation, showcases enhanced capabilities of fire recognition to improve maritime safety. To train the model, we collected 4622 images of various ship scenarios and performed data augmentation techniques such as rotation, horizontal and vertical flips, and scaling. Its enhanced feature fusion technique makes it superior to all its predecessors. The YOLOv7 architecture, with an improved E-ELAN (extended efficient layer aggregation network) as its backbone, serves as the basis of our fire detection system. The study in this research paper presents a fire detection technique based on YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. Therefore, timely detection of fires is essential for quick responses and powerful mitigation.

Fires on ships can have extensive and severe wide-ranging impacts on matters such as the safety of the crew, cargo, the environment, finances, reputation, etc. Fire incidents occurring onboard ships cause significant consequences that result in substantial effects.

Instaling Forest Fire