Zhu Jinting, Zhang Jundong, Wang Yongkang, Ge Yuequn, Zhang Ziwei, Zhang Shihan
College of Marine Engineering, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel). 2023 Jul 20;23(14):6552. doi: 10.3390/s23146552.
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.
船舶火灾是危及船舶安全的主要因素之一；由于船舶远离陆地，火灾可能难以扑灭，且常常会造成巨大损失。机舱有许多设备，是火灾的主要发生地点；然而，由于其内部环境复杂，会给火灾探测工作带来诸多困难。传统的探测方法有其自身局限性，但利用深度学习技术进行火灾探测具有探测速度快和准确率高的特点。在本文中，我们对YOLOv7-tiny模型进行改进以提升其探测性能。首先，将部分卷积（PConv）和坐标注意力（CA）机制引入模型，以提高其探测速度和特征提取能力。然后，使用SIoU作为损失函数来加速模型收敛并提高准确率。最后，我们在自制的船舶机舱火灾数据集上的实验结果表明，改进模型的mAP@0.5提高了2.6%，速度提高了10帧每秒，能够满足机舱火灾探测的需求。
