Smoke Detection of Marine Engine Room Based on a Machine Vision Model (CWC-Yolov5s)

• Published in: Journal of marine science and engineering Vol. 11; no. 8; p. 1564
• Main Authors: Zou, Yongjiu, Zhang, Jinqiu, Du, Taili, Jiang, Xingjia, Wang, Hao, Zhang, Peng, Zhang, Yuewen, Sun, Peiting
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Accuracy; Algorithms; Codes; Computer vision; Convolution; Crew; Datasets; Deep learning; Detection; Engine rooms; Engines; Fires; Forest & brush fires; Image enhancement; improved YOLOv5s model; intelligent ship; Internet of Things; Machine vision; Marine accidents; Marine engines; Model accuracy; Object recognition; Regression models; Research methodology; Smoke; smoke detection; Statistical methods; Vision systems; Wavelet transforms
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse11081564

According to statistics, about 70% of ship fire accidents occur in the engine room, due to the complex internal structure and various combustible materials. Once a fire occurs, it is difficult to extinguish and significantly impacts the crew’s life and property. Therefore, it is urgent to design a method to detect the fire phenomenon in the engine room in real time. To address this problem, a machine vision model (CWC-YOLOv5s) is proposed, which can identify early fires through smoke detection methods. Firstly, a coordinate attention mechanism is added to the backbone of the baseline model (YOLOv5s) to enhance the perception of image feature information. The loss function of the baseline model is optimized by wise intersection over union, which speeds up the convergence and improves the effect of model checking. Then, the coordconv coordinate convolution layer replaces the standard convolution layer of the baseline model, which enhances the boundary information and improves the model regression accuracy. Finally, the proposed machine vision model is verified by using the ship video system and the laboratory smoke simulation bench. The results show that the proposed model has a detection precision of 91.8% and a recall rate of 88.1%, which are 2.2% and 4.6% higher than those of the baseline model.