Fire Detection in Ship Engine Rooms Based on Deep Learning

Abstract

Ship fires are one of the main factors that endanger the safety of ships; because the ship is far away from land, the fire can be difficult to extinguish and could often cause huge losses. The engine room has many pieces of equipment and is the principal place of fire; however, due to its complex internal environment, it can bring many difficulties to the task of fire detection. The traditional detection methods have their own limitations, but fire detection using deep learning technology has the characteristics of high detection speed and accuracy. In this paper, we improve the YOLOv7-tiny model to enhance its detection performance. Firstly, partial convolution (PConv) and coordinate attention (CA) mechanisms are introduced into the model to improve its detection speed and feature extraction ability. Then, SIoU is used as a loss function to accelerate the model’s convergence and improve accuracy. Finally, the experimental results on the dataset of the ship engine room fire made by us shows that the mAP@0.5 of the improved model is increased by 2.6%, and the speed is increased by 10 fps, which can meet the needs of engine room fire detection.

Ship-Fire Net: An Improved YOLOv8 Algorithm for Ship Fire Detection

“…Kim et al [44] proposed an innovative method that integrates composite channel data, namely, RGB and IR channels, to enhance the effectiveness of fire detection in image-based systems by using convolutional neural networks (CNNs); Park et al [19] used YOLO to detect fires in the engine room of a ship. Xu et al [20] proposed an evaluation model based on a CNN that could determine the hazard levels of different cabins in a realtime fire; Wu et al [21] proposed a modified YOLOv4-tiny algorithm for detecting ship fires; Avazov et al [22] used YOLOv7 with an improved E-ELAN (extended efficient layer aggregation network) for fire detection and monitoring; and Zhu et al [45] improved the YOLOv7-tiny model to enhance fire detection performance in a ship engine room. However, these studies have several limitations, which are shown in Table 1.…”

Advancing Maritime Safety: Early Detection of Ship Fires through Computer Vision, Deep Learning Approaches, and Histogram Equalization Techniques

“…The pivotal compartment of a vessel responsible for powering and ensuring its seamless operation is the engine room. Nevertheless, owing to its intricate structure and the presence of flammable materials, 75% of all ship fires originate in the engine room and nearly two-thirds of these engine room fires specifically occur in the primary and auxiliary engines, as well as in closely associated components such as turbochargers [2]. Given this context, the detection of engine room fires holds paramount significance.…”

DCP-Net: An Efficient Image Segmentation Model for Forest Wildfires

“…Xue et al [32] modified the Spatial Pyramid Pooling-Fast (SPPF) module from YOLOv5 to develop the Spatial Pyramid Pooling-Fast-Plus (SPPFP) module specifically for fire detection, achieving a 10.1% improvement in mAP@0.5 on their dataset. Zhu et al [33] used an improved YOLOv7-tiny model to detect cabin fires, resulting in a 2.6% increase in mAP@0.5 and a 10 fps increase in frame rate. Hojune Ann et al [34] developed a fire risk detection system that detects fire sources and combustible materials simultaneously by object detection on images captured by surveillance cameras, comparing the performance of two deep learning models, YOLOv5 and EfficientDet.…”