An integrated fire detection system using IoT and image processing technique for smart cities

2020 ◽  
Vol 61 ◽  
pp. 102332 ◽  
Author(s):  
Amit Sharma ◽  
Pradeep Kumar Singh ◽  
Yugal Kumar
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Fire Detection System

REAL-TIME FIRE AND SMOKE DETECTION USING CAMERA SEQUENCE IMAGE IN TUNNEL ENVIRONMENT

2007 ◽  
Vol 04 (04) ◽  
pp. 327-338 ◽  
Author(s):  
BYOUNGMOO LEE ◽  
DONGIL HAN
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Large Scale ◽  
Detection System ◽  
Fire Detection ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Smoke Detection ◽  
Forest Environment ◽  
Exact Position

In this paper, we proposed an image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurring in the tunnel, it is necessary to have a system to sense and minimize the incident as fast as possible. However it is impossible for human observation of Closed-Circuit Television (CCTV) in tunnel for 24 h. So if the fire and smoke detection system through image processing can warn a fire, it will be very convenient, and it can be possible to minimize damage even when no one is in front of the monitor. The fire and smoke detection is different from forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so an indigenous algorithm has to be developed. The two algorithms proposed in this paper are able to detect the exact position at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm.

scholarly journals An Image Processing Based Fungus Detection System for Mangoes

2019 ◽  
Vol 9 (1) ◽  
pp. 3493-3497
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Health Problems ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
High Yield ◽  
Data Set ◽  
Different Color ◽  
The Given

Fruits which grow with high yield in many states of India are rich in proteins. But due to addition of excess pesticides and chemicals intake of these fruits lead to serious health problems. It is necessary to identify the presence of chemical in the fruits before consuming it. In this project we have planned to develop an image processing technique to analyze whether the fruit is free from chemicals and fungus. In our paper, we have implemented MATLAB used as well as fungus present in the fruit. We capture the images of the fruit or we use datasets and train the database with different color-based changes that happen after adding chemicals to the fruit. The enhancement process is carried out in the captured image. Then image is segmented to hit the regions with affected spots in the fruit. K-means method is used to carry out the segmentation process. The input image is compared with the given data set for training to identify the images. In this way unhealthy fruits can be identified and the affected spots in the fruit can be detected.

scholarly journals Real Time Fire Detection System with Image Processing

2021 ◽  
Vol 3 ◽  
pp. 19-24
Author(s):  
Min Thu Soe ◽  
Thein Oak Kyaw Zaw ◽  
Wai Kit Wong
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Fire Detection ◽  
Accurate Method ◽  
False Alarms ◽  
Thermal Camera ◽  
Fire Fighters ◽  
The People ◽  
Fire Detection System ◽  
Thermal Video

Fire detectionsystemby image processing is a growing research in this era. There are many methods used to detect fire out, butstill need to develop an accurate method to detect fire without false alarms. This is due to the fact that many methods used RGB colour mode for detection. In this paper, mainly focuson detecting the fire effectively using thermal video from a thermal camera while in the same time the system will alert the people if fire was detected,and also observed the speed of the fire.This will enormouslybenefitto the fire fighters.With thissystem, thefire can be detected effectively while alerting the people and giving valuable information to the fire fighters fortheir job more effectively.

scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

scholarly journals Detection of Traffic Light using Machine Vision for Autonomous Vehicles Application

2019 ◽  
Vol 9 (2) ◽  
pp. 1033-1037
Keyword(s):  
Image Processing ◽  
Urban Areas ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Traffic Light ◽  
Light Detection ◽  
Traffic Lights

Traffic light detection is crucial to decrease the traffic light accidents at intersections and to realize autonomous driving. There are so many existing methods to detect traffic light. However, these approaches have several limitations, such as not function well in complex driving environments. Hence, to overcome such constraints, the traffic light detection for the autonomous vehicle using image processing technique is proposed. The experiments are carried out using 114 scene images that consist of 209 traffic lights with different angles, weather conditions, and distance. An image processing technique, Hough Circle Transform is used in this traffic light detection system with the help of Gaussian blurring and Sobel filter. So, the overall accuracy rate for the proposed algorithm is 75.59%. This system is possible to be used in urban areas or complex environments, whether it is at night or day, and it able to detect the traffic light regardless of the colour changes.

Design of a Real-Time Video-Fire-Detection System Based on DSP

2011 ◽  
Vol 52-54 ◽  
pp. 1137-1141
Author(s):  
W. Yuan ◽  
J. Li ◽  
J. Fang ◽  
H.B. Hu ◽  
Y.M. Zhang
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Fire Detection ◽  
Circuit Board ◽  
Open Spaces ◽  
Fire Alarm ◽  
Video Acquisition ◽  
Portable System ◽  
Fire Detection System ◽  
Indoor And Outdoor

Many methods of fire detection in video have been carried out on PC. Generally it needs long cables to transmit images and processes videos on computers. That system is too dependent on computers, and costs a lot using cables to transmission. We would rather use a portable device to detect fire, which will bring us much more convenience. This paper designed a portable system to detect fire using DSP. The system integrates video acquisition, image processing and fire alarm together in a circuit board. Then we develop algorithms to recognize fire, and transplant them into DSP. Finally put it into use after commissioning test. This portable system can work independently. Users can put into their requirements through user-interface, and system will output the fire pictures and alarm by wireless communication or internet. This system is applicable to various places, such as indoor and outdoor, large and open spaces, forest, etc.

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