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.

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.

Network-centric approach to prevention and elimination of forest fires based on integration information resources and systems

2020 ◽  
Vol 89 ◽  
pp. 98-109
Author(s):  
S. A. Barkovsky ◽  
◽  
A. V. Spiridonov ◽  
A. I. Ovsyanik ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Monitoring System ◽  
Forest Fire ◽  
Forest Fires ◽  
Information Resources ◽  
Fire Monitoring ◽  
Digital Maps ◽  
Emergency Situations ◽  
Operational Information ◽  
Current Situation Analysis ◽  
Forest Fire Prevention

Introduction. The article analyzes the use of modern information resources and systems in extinguishing wildfires. The approach uses a network-centric system for modern resources and systems integration. Goals and objectives. The purpose of the study is to identify and improve methods and tools for assessing forest fire prevention based on the integration of information resources and systems. Methods. Information-based methods and systems that allow simultaneous analysis of multi-dimensional data using digital maps, simplify the prediction and assessment of the complex effects of forest fires are considered, make it possible to quickly identify the anomalies and take the necessary measures to eliminate them. Results and discussion. Based on the results of the analysis, a generalized structure of the forest fire monitoring system was developed using a network-centric approach. Conclusion. A network-centric monitoring system combines all information resources and systems at all levels and directions. Early monitoring of the current situation, analysis of current operational information about identified thermal points, and modeling of possible scenarios for the development of the situation allows the most effective approach to solving problems of protecting the population and territory from the consequences of forest fires. Combining the advantages of individual modern technologies into a single distributed network-centric system makes it possible to effectively implement the elimination of the consequences of all types of emergencies. Key words: network-centric system, forest fires, forecasting and prevention of emergency situations, information resources and systems.

scholarly journals Improvement of forest fire monitoring system by expanding information and technological possibilities of modern quadcopters

2021 ◽  
pp. 33-46
Author(s):  
Olena Husak ◽  
Volodymyr Husak
Keyword(s):  
Information Technology ◽  
Early Detection ◽  
Unmanned Aerial Vehicles ◽  
Monitoring System ◽  
Forest Fire ◽  
Fire Protection ◽  
Forest Fires ◽  
High Tech ◽  
Fire Monitoring ◽  
Aerial Vehicles

The article proposes a solution to an important problem — the development of an information technology based on expanding the functionality of non-specialized unmanned aerial vehicles (drones) for early detection of forest fires. The proposed information technology is designed to increase the effectiveness of monitoring forest fires. Тhe existing level of information technology does not fully settle the issue of reliable fire protection of forests. Today, there is a contradiction between the high cost of developing high-tech fire-fighting equipment and lack of its efficiency. The elimination of this contradiction will be facilitated by the involvement of additional non-technical and technical resources in the information technology of early detection of forest fire hotspots. The results of the analysis of the use of modern drones prove that the involvement of unmanned aerial vehicles significantly increases the efficiency of many types of monitoring and they can successfully be used to solve the problems of early detection of forest fire hotspots. The results of experiments are presented, which were carried out both for a series of digital images and for video.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

scholarly journals An Environmentally Aware Scheme of Wireless Sensor Networks for Forest Fire Monitoring and Detection

2018 ◽  
Vol 10 (10) ◽  
pp. 102 ◽  
Author(s):  
Yi-Han Xu ◽  
Qiu-Ya Sun ◽  
Yu-Tong Xiao
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Forest Fire ◽  
Forest Fires ◽  
Network Architecture ◽  
Resource Constraints ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Fire Monitoring

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.

scholarly journals Energy-Efficient Forest Fire Prediction Model based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol

2018 ◽  
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Jin-Woo Jung
Keyword(s):  
Prediction Model ◽  
Forest Fire ◽  
Duty Cycle ◽  
Forest Fires ◽  
Energy Efficient ◽  
Mac Protocol ◽  
Two Stage ◽  
Fire Monitoring ◽  
Environmental Status ◽  
Mountain Environment

In this paper, we propose an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The X-MAC protocol acquires the additional environmental status collected by each forest fire monitoring sensor for a certain period. And, based on these values, the length of sleep interval of duty-cycle is changed to efficiently calculate the risk of occurrence of forest fire according to the mountain environment. The performance of the proposed ADX-MAC protocol was verified through experiments the proposed ADX-MAC protocol improves throughput by 19% and was more energy-efficient by 24% compared to X-MAC protocol. As the probability of forest fires increases, the length of the duty cycle is shortened, confirming that the forest fires are detected at a faster cycle.

A Vision-Based Hygiene Monitoring System: Using Deep Learning to Assess Handwashing Procedure in Real Time

2019 ◽  
Author(s):  
Maria Galkin ◽  
Kashmala Rehman ◽  
Benjamin Schornstein ◽  
Warren Sunada-Wong ◽  
Harvey Wang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Monitoring System
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