Open AccessSmoke detection in ship engine rooms based on video images
Author(s) -
Park KyungMin,
Bae CherlO
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5305
Subject(s) - smoke , computer science , grayscale , artificial intelligence , fire detection , computer vision , support vector machine , motion detection , feature extraction , pattern recognition (psychology) , engineering , pixel , motion (physics) , waste management , architectural engineering
Fire detection systems in ships are based on smoke and heat detection in accordance with safety regulations. The rapid advancement of machine vision technology has led to the development of video smoke detection (VSD) systems. In this study, a VSD system is applied to smoke detection within the engine room of the ship. A dataset for a range of scenarios was created with a smoke generator. The method for smoke detection was based on motion detection and a support vector machine classifier, which was used to make candidate regions and perform classification. A local binary pattern descriptor was used to extract the feature vector. A training set was made from a variety of video frames, randomly. Experimental results seldom produced false positive windows in the non‐smoke region. However, if the greyscale value of difference image between background and the smoke is lower than the setting value for motion detection, the system could not detect smoke. Processing time is sufficiently fast for use in real‐time smoke detection systems. To install a VSD system on‐board a vessel, the authors recommend a performance standard of the system which must be met.