Use of night vision goggles for aerial forest fire detection

2014 ◽  
Vol 23 (5) ◽  
pp. 678 ◽  
Author(s):  
L. Tomkins ◽  
T. Benzeroual ◽  
A. Milner ◽  
J. E. Zacher ◽  
M. Ballagh ◽  
...  
Keyword(s):  
Forest Fires ◽  
Bright Light ◽  
Fire Detection ◽  
Point Sources ◽  
Night Vision ◽  
Line Of Sight ◽  
Night Time ◽  
Hit Rate ◽  
Potential Source ◽  
Clear Line

Night-time flight searches using night vision goggles have the potential to improve early aerial detection of forest fires, which could in turn improve suppression effectiveness and reduce costs. Two sets of flight trials explored this potential in an operational context. With a clear line of sight, fires could be seen from many kilometres away (on average 3584m for controlled point sources and 6678m for real fires). Observers needed to be nearer to identify a light as a potential source worthy of further investigation. The average discrimination distance, at which a source could be confidently determined to be a fire or other bright light source, was 1193m (95% CI: 944 to 1442m). The hit rate was 68% over the course of the controlled experiment, higher than expectations based on the use of small fire sources and novice observers. The hit rate showed improvement over time, likely because of observers becoming familiar with the task and terrain. Night vision goggles enable sensitive detection of small fires, including those that were very difficult to detect during daytime patrols. The results demonstrate that small fires can be detected and reliably discriminated at night using night vision goggles at distances comparable to those recorded for daytime aerial detection patrols.

scholarly journals Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm

2018 ◽  
Author(s):  
Valeria Di Biase ◽  
Giovanni Laneve
Keyword(s):  
Forest Fires ◽  
Elevation Angle ◽  
Fire Detection ◽  
High Sensitivity ◽  
Low Earth Orbit ◽  
False Alarms ◽  
Commission Error ◽  
Night Time ◽  
Thermal Anomalies ◽  
Mediterranean Countries

The paper aims at presenting the results obtained in the development of a system allowing the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously, as usually happens in the European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection), exploits a geostationary satellite sensor (SEVIRI on board of MSG satellite series). The algorithm was developed several years ago in the framework of a project (SIGRI) funded by the Italian Space Agency (ASI). This algorithm has been completely reviewed in order to enhance its efficiency by reducing false alarms rate preserving a high sensitivity. Due to the very low spatial resolution of SEVIRI images (4x4 km2 at Mediterranean latitude) the sensitivity of the algorithm should be very high to detect even small fires. The improvement of the algorithm has been obtained by: introducing the sun elevation angle in the computation of the preliminary thresholds to identify potential thermal anomalies (hot spots), introducing a contextual analysis in the detection of clouds and in the detection of night-time fires. The results of the algorithm have been validated in the Sardinia region by using ground true data provided by the regional Corpo Forestale e di Vigilanza Ambientale (CFVA). A significant reduction of the commission error (less than 10%) has been obtained with respect to the previous version of the algorithm and also with respect to fire-detection algorithms based on low earth orbit satellites.

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

scholarly journals Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Hai Wang ◽  
Yingfeng Cai ◽  
Xiaobo Chen ◽  
Long Chen
Keyword(s):  
Detection Rate ◽  
False Positive Rate ◽  
Infrared Image ◽  
Far Infrared ◽  
Night Vision ◽  
Infrared Sensors ◽  
Night Time ◽  
Detection Rates ◽  
Positive Rate ◽  
And Performance

The use of night vision systems in vehicles is becoming increasingly common. Several approaches using infrared sensors have been proposed in the literature to detect vehicles in far infrared (FIR) images. However, these systems still have low vehicle detection rates and performance could be improved. This paper presents a novel method to detect vehicles using a far infrared automotive sensor. Firstly, vehicle candidates are generated using a constant threshold from the infrared frame. Contours are then generated by using a local adaptive threshold based on maximum distance, which decreases the number of processing regions for classification and reduces the false positive rate. Finally, vehicle candidates are verified using a deep belief network (DBN) based classifier. The detection rate is 93.9% which is achieved on a database of 5000 images and video streams. This result is approximately a 2.5% improvement on previously reported methods and the false detection rate is also the lowest among them.

Modelling the effects of distance on the probability of fire detection from lookouts

2006 ◽  
Vol 15 (2) ◽  
pp. 197 ◽  
Author(s):  
Francisco Castro Rego ◽  
Filipe Xavier Catry
Keyword(s):  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Fast Response ◽  
Fire Loss ◽  
Detection Function ◽  
Related Factors ◽  
New Approach ◽  
Forest Fire Management ◽  
In Fire

In the management of forest fires, early detection and fast response are known to be the two major actions that limit both fire loss and fire-associated costs. There are several inter-related factors that are crucial in producing an efficient fire detection system: the strategic placement and networking of lookout towers, the knowledge of the fire detection radius for lookout observers at a given location and the ability to produce visibility maps. This study proposes a new methodology in the field of forest fire management, using the widely accepted Fire Detection Function Model to evaluate the effect of distance and other variables on the probability that an object is detected by an observer. In spite of the known variability, the model seems robust when applied to a wide variety of situations, and the results obtained for the effective detection radius (13.4 km for poor conditions and 20.6 km for good conditions) are in general agreement with those proposed by other authors. We encourage the application of the new approach in the evaluation or planning of lookout networks, in addition to other integrated systems used in fire detection.

scholarly journals Subtraction of Bright Point Sources from Synthesis Images of the Epoch of Reionization

2011 ◽  
Vol 28 (1) ◽  
pp. 46-57 ◽  
Author(s):  
B. Pindor ◽  
J. S. B. Wyithe ◽  
D. A. Mitchell ◽  
S. M. Ord ◽  
R. B. Wayth ◽  
...  
Keyword(s):  
Active Galactic Nuclei ◽  
Dynamic Range ◽  
Low Frequency ◽  
Neutral Hydrogen ◽  
Galactic Nuclei ◽  
Point Sources ◽  
Field Of View ◽  
Bright Point ◽  
Line Of Sight ◽  
Diffuse Background

AbstractBright point sources associated with extragalactic active galactic nuclei and radio galaxies are an important foreground for low-frequency radio experiments aimed at detecting the redshifted 21-cm emission from neutral hydrogen during the epoch of reionization. The frequency dependence of the synthesized beam implies that the sidelobes of these sources will move across the field of view as a function of observing frequency, hence frustrating line-of-sight foreground subtraction techniques. We describe a method for subtracting these point sources from dirty maps produced by an instrument such as the MWA. This technique combines matched filters with an iterative centroiding scheme to locate and characterize point sources in the presence of a diffuse background. Simulations show that this technique can improve the dynamic range of epoch-of-reionization maps by 2—3 orders of magnitude.

scholarly journals GOMOS ozone profile validation using ground-based and balloon sonde measurements

2010 ◽  
Vol 10 (21) ◽  
pp. 10473-10488 ◽  
Author(s):  
J. A. E. van Gijsel ◽  
D. P. J. Swart ◽  
J.-L. Baray ◽  
H. Bencherif ◽  
H. Claude ◽  
...  
Keyword(s):  
Selection Criteria ◽  
Polar Region ◽  
Temporal Trend ◽  
Microwave Radiometer ◽  
Line Of Sight ◽  
Night Time ◽  
Latitudinal Dependence ◽  
Ozone Profile ◽  
The Tropics ◽  
Level 2

Abstract. The validation of ozone profiles retrieved by satellite instruments through comparison with data from ground-based instruments is important to monitor the evolution of the satellite instrument, to assist algorithm development and to allow multi-mission trend analyses. In this study we compare ozone profiles derived from GOMOS night-time observations with measurements from lidar, microwave radiometer and balloon sonde. Collocated pairs are analysed for dependence on several geophysical and instrument observational parameters. Validation results are presented for the operational ESA level 2 data (GOMOS version 5.00) obtained during nearly seven years of observations and a comparison using a smaller dataset from the previous processor (version 4.02) is also included. The profiles obtained from dark limb measurements (solar zenith angle >107°) when the provided processing flag is properly considered match the ground-based measurements within ±2 percent over the altitude range 20 to 40 km. Outside this range, the pairs start to deviate more and there is a latitudinal dependence: in the polar region where there is a higher amount of straylight contamination, differences start to occur lower in the mesosphere than in the tropics, whereas for the lower part of the stratosphere the opposite happens: the profiles in the tropics reach less far down as the signal reduces faster because of the higher altitude at which the maximum ozone concentration is found compared to the mid and polar latitudes. Also the bias is shifting from mostly negative in the polar region to more positive in the tropics Profiles measured under "twilight" conditions are often matching the ground-based measurements very well, but care has to be taken in all cases when dealing with "straylight" contaminated profiles. For the selection criteria applied here (data within 800 km, 3 degrees in equivalent latitude, 20 h (5 h above 50 km) and a relative ozone error in the GOMOS data of 20% or less), no dependence was found on stellar magnitude, star temperature, nor the azimuth angle of the line of sight. No evidence of a temporal trend was seen either in the bias or frequency of outliers, but a comparison applying less strict data selection criteria might show differently.

scholarly journals Eye Controlled Robotic Chair with Night Vision Camera

2018 ◽  
Vol 7 (2.7) ◽  
pp. 536
Author(s):  
G Harish Babu ◽  
Dr N. Venkatram
Keyword(s):  
Ultrasonic Sensor ◽  
Night Vision ◽  
Light Conditions ◽  
Night Time ◽  
Emergency Situations ◽  
The People ◽  
Wheel Chair

According to Census 2011, in India the population is 121 Cr, from the aggregate residents 2.68 Cr individuals are impaired i.e 2.21 percent of the aggregate residents. From the 2.68 Cr impaired individuals 20 percent of the people disability in movement. Our project is an attempt to make lives of the people suffering from this phenomenon simple. In this framework controlling of wheel chair is depends upon the movements of patient’s Eye [1].  A camera is placed before the user, to catch the picture of the eye & footmarks the locale of eye pupil.  Camera used here is Night vision camera which works during night time and as well as daytime and will also work during low lightings. As per the position of eye pupil, wheel chair will be coordinated to move left, appropriate, forward and in reverse. The previous algorithms which are developed for this type of project doesn’t support for the people with squint eye and supports only for the people with 2 perfect pupils, and those algorithms are works only in clear light conditions but this project supports for the people with squint eye as well as people with 2 perfect pupils and works during low lightings. The person can stop the chair by closing his eyes whenever he needs. Moreover, a ultrasonic sensor is ascended in front, so that it can recognize the snags and naturally stop the wheel chair. A GPS module is inserted which finds the location of the wheel chair. A GSM module is inserted which sends the messages in case of emergency situations.  This project provides an option called voice module which is inserted and works based on the commands of the patients.  

scholarly journals Internet of Trees

2021 ◽  
Vol 10 (2) ◽  
pp. 750-753
Keyword(s):  
Forest Fires ◽  
Low Cost ◽  
Web Server ◽  
Fire Risk ◽  
Fire Detection ◽  
Natural Habitats ◽  
The Public ◽  
Recorded Data ◽  
The Impact ◽  
The Internet Of Things

The frequency of the forest fires that have occurred in the different parts of the world, In recent decades significant population problems and causing the death if the wild animals as the impact of these fires extend beyond the destruction of the natural habitats. The proliferation of the Internet of Things industry, resolutions for initial fire detection should be developed. The valuation of the fire risk of an area and communication of this realities to the population could reduce the amount of fires originated by accident or due to carelessness of the public user. This paper proposes a low-cost network based on NXP Rapid IOT kit and Long Range (Lora) technology to autonomously estimate the level of fire risk in the forest. The system comprises of NXP Rapid IOT kit which humidity, air quality and detection of the tree fall. The data from each node stored and processed in a in a web server or the mobile application that sendsthe recorded data to a web server for graphical conception of collected data.

First study of Sentinel-3 SLSTR active fire detection and FRP retrieval: Night-time algorithm enhancements and global intercomparison to MODIS and VIIRS AF products

2020 ◽  
Vol 248 ◽  
pp. 111947
Author(s):  
Weidong Xu ◽  
Martin J. Wooster ◽  
Jiangping He ◽  
Tianran Zhang
Keyword(s):  
Fire Detection ◽  
Time Algorithm ◽  
Night Time ◽  
Active Fire

scholarly journals Early smoke detection of forest fires based on SVM image segmentation

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 150-159
Author(s):  
Ding Xiong ◽  
Lu Yan
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Smoke Detection ◽  
Large Space ◽  
Merging Algorithm ◽  
Detection Technology

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

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