A real-time deep learning forest fire monitoring algorithm based on an improved Pruned + KD model

2021 ◽  
Author(s):  
Shengying Wang ◽  
Jing Zhao ◽  
Na Ta ◽  
Xiaoye Zhao ◽  
Mingxia Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Forest Fire ◽  
Fire Monitoring ◽  
Monitoring Algorithm

scholarly journals Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shaoxiong Zheng ◽  
Weixing Wang ◽  
Zeqian Liu ◽  
Zepeng Wu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Forest Fire ◽  
Forest Fires ◽  
Operating Cost ◽  
Operating Efficiency ◽  
Rescue Operation ◽  
Fire Monitoring ◽  
Forest Farm

Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire monitoring, this paper proposes a forest fire monitoring system based on drones and deep learning. The proposed system aims to solve the shortcomings of traditional forest fire monitoring systems, such as blind spots, poor real-time performance, expensive operational costs, and large resource consumption. The image processing techniques are used to determine whether the frame returned by a drone contains fire. This process is accomplished in real time, and the resultant information is used to decide whether a rescue operation is needed. The proposed method has simple operations, high operating efficiency, and low operating cost. The experimental results indicate that the relative accuracy of the proposed algorithm is 81.97%. In addition, the proposed technique provides a digital ability to monitor forest fires in real time effectively. Thus, it can assist in avoiding fire-related disasters and can significantly reduce the labor and other costs of forest fire disaster prevention and suppression.

scholarly journals A Study on the Real-time Fire Classification Algorithm Based on Stacked LSTM

2021 ◽  
Vol 21 (3) ◽  
pp. 93-104
Author(s):  
Yoseob Heo ◽  
Seongho Seo ◽  
We Shim ◽  
Jongseok Kang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Dependence ◽  
Real Time ◽  
Sensor Data ◽  
Fire Monitoring ◽  
Monitoring Framework ◽  
Sensor Signals ◽  
Fire Detector ◽  
Using Data

Several researchers have been drawn to the development of fire detector in recent years, to protect people and property from the catastrophic disaster of fire. However, studies related to fire monitoring are affected by some unique characteristics of fire sensor signals, such as time dependence and the complexity of the signal pattern based on the variety of fire types,. In this study, a new deep learning-based approach that accurately classifies various types of fire situations in real-time using data obtained from multidimensional channel fire sensor signals was proposed. The contribution of this study is to develop a stacked-LSTM model that considers the time-series characteristics of sensor data and the complexity of multidimensional channel sensing data to develop a new fire monitoring framework for fire identification based on improving existing fire detectors.

Development of Forest Fire Monitoring System Using a Long-Term Endurance Solar Powered Drone and Deep Learning

2020 ◽  
Vol 28 (2) ◽  
pp. 29-38
Author(s):  
Dong Ki Chung ◽  
Myung Hwa Lee ◽  
Hwi Young Kim ◽  
Jeong Yong Park ◽  
Im Pyeong Lee
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Forest Fire ◽  
Fire Monitoring ◽  
Solar Powered

scholarly journals A YOLO based Technique for Early Forest Fire Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1357-1362
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Forest Fire ◽  
Forest Fires ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Mobile App ◽  
Raspberry Pi ◽  
Time Data ◽  
Alert Message

Forest fires, wildfires and bushfires are a global environmental problem that causes serious damage each year. The most significant factors in the fight against forest fires involve earliest possible detection of the fire, flame or smoke event, proper classification of the fire and rapid response from the fire departments. In this paper, we developed an automatic early warning system that incorporates multiple sensors and state of the art deep learning algorithm which has a minimum number of false positives and give a good accuracy in real time data and in the lowest cost possible to our drone to monitor forest fire as early as possible and report it to the concerned authority. The drones will be equipped with sensors, Raspberry pi 3, neural stick, APM 2.5, GPS, Wifi. The neural stick will be used for real time image processing using our state-of-the-art deep learning model. And as soon as forest fire is detected the UAV will send an alert message to the concerned authority on the mobile App along with location coordinates of the fire, image and the amount of area in which forest is spread using a mesh messaging. So that immediate action will be taken to stop it from spreading and causing loss of millions of lives and money. Using both deep learning and infrared cameras to monitor the forest and surrounding area, we will take advantage of recent advances in multi-sensor surveillance technologies. This innovative technique helps the forest department to detect fire in first 12 hours of its initialization , which is the most effective time to control the fire.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm
Sign in / Sign up

Export Citation Format

Share Document