NDLI: Smoke detection in ship engine rooms based on video images

Content Provider	IET Digital Library
Author	Park, Kyung Min Bae, Cherl O
Abstract	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.
Starting Page	1141
Ending Page	1149
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 6, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5305
Journal	IET Image Processing
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-17
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Computer Vision Computer Vision And Image Processing Technique Feature Extraction Feature Vector Extraction Fire Detection System Fire Safety Heat Detection Image Classification Image Motion Analysis Image Recognition Knowledge Engineering Technique Local Binary Pattern Descriptor Machine Vision Motion Detection Nonsmoke Region Object Detection Real-time Smoke Detection System Ship Engine Rooms Ships Smoke Smoke Detector Smoke Generator Support Vector Machine Support Vector Machine Classifier Video Frames Video Image Video Signal Processing Video Smoke Detection System VSD System
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Fight detection by using motion co-occurrence feature

Forensic video solution using facial feature-based synoptic Video Footage Record

RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

Fast ship contour extraction in SAR images

Fusing texture, edge and line features for smoke recognition

Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Automatic underwater moving object detection using multi-feature integration framework in complex backgrounds

Density aware anomaly detection in crowded scenes

Frame duplication/mirroring detection method with binary features

Smoke detection in ship engine rooms based on video images

Similar Documents

Fight detection by using motion co-occurrence feature

Forensic video solution using facial feature-based synoptic Video Footage Record

RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

Fast ship contour extraction in SAR images

Fusing texture, edge and line features for smoke recognition

Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Automatic underwater moving object detection using multi-feature integration framework in complex backgrounds

Density aware anomaly detection in crowded scenes

Frame duplication/mirroring detection method with binary features

Smoke detection in ship engine rooms based on video images