Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Multi-modal video analysis for early fire detection

In dit proefschrift worden verschillende aspecten van een intelligent videogebaseerd branddetectiesysteem onderzocht. In een eerste luik ligt de nadruk op de multimodale verwerking van visuele, infrarood en time-of-flight videobeelden, die de louter visuele detectie verbetert. Om de verwerkingskost zo minimaal mogelijk te houden, met het oog op real-time detectie, is er voor elk van het type sensoren een set ’low-cost’ brandkarakteristieken geselecteerd die vuur en vlammen uniek beschrijven. Door het samenvoegen van de verschillende typen informatie kunnen het aantal gemiste detecties en valse alarmen worden gereduceerd, wat resulteert in een significante verbetering van videogebaseerde branddetectie. Om de multimodale detectieresultaten te kunnen combineren, dienen de multimodale beelden wel geregistreerd (~gealigneerd) te zijn. Het tweede luik van dit proefschrift focust zich hoofdzakelijk op dit samenvoegen van multimodale data en behandelt een nieuwe silhouet gebaseerde registratiemethode. In het derde en tevens laatste luik van dit proefschrift worden methodes voorgesteld om videogebaseerde brandanalyse, en in een latere fase ook brandmodellering, uit te voeren. Elk van de voorgestelde technieken voor multimodale detectie en multi-view lokalisatie zijn uitvoerig getest in de praktijk. Zo werden onder andere succesvolle testen uitgevoerd voor de vroegtijdige detectie van wagenbranden in ondergrondse parkeergarages

Video fire detection - Review

Cataloged from PDF version of article.This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor “volumes” and do not have transport delay that the traditional “point” sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tiltzoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation. © 2013 Elsevier Inc. All rights reserve

Study Of Pool Fire Heat Release Rate Using Video Fire Detection

To provide fire safety for high performance buildings, various types of fire/smoke detection systems are developed. Video fire detection is one of the important aspects in the development of fire detection system. It is particularly useful in large spaces with high headroom and buildings with cross ventilation design where traditional spot type smoke detection methods may not be effective. For the development of video fire detection system, spatial, spectral and temporal parameters are used to identify the fire source. One of the parameters captured by the video fire detection system is the flame height. With the information of flame height, real time heat release rate of fire can be estimated which is a very important parameter in determining the smoke generation rate and fire severity. Such information is very important in assisting evacuation and smoke control. In this study, experiments of pool fires with different pool diameters of 100mm, 200mm, 300mm and 400mm are conducted in the fire chamber of the laboratory in Department of Building Services Engineering, The Hong Kong Polytechnic University. The flame images, room temperatures and mass loss rates of the fuel are measured. The flame images are segmented using multi – threshold algorithm in a modified Otsu method and Rayleigh distribution analysis (modified segmentation algorithm). The algorithm use the optimum threshold values calculated to extract the pool fire images from a video sequence. After segmentation, flame height information can be obtained. In addition, other flame characteristics are also used for recognizing the flame region including flame color, flame light intensity, flame shape, and flicker frequency. Once the flame height is identified by the system, the heat release rate can be estimated using the equation developed by McCaffrey. The calculated heat release rates are then compared with measured heat release rate data. The results show that using flame height image for estimating real time heat release rate is promising

Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios

In response to the existing object detection algorithms are applied to complex fire scenarios with poor detection accuracy, slow speed and difficult deployment., this paper proposes a lightweight fire detection algorithm of Light-YOLOv5 that achieves a balance of speed and accuracy. First, the last layer of backbone network is replaced with SepViT Block to enhance the contact of backbone network to global information; second, a Light-BiFPN neck network is designed to lighten the model while improving the feature extraction; third, Global Attention Mechanism (GAM) is fused into the network to make the model more focused on global dimensional features; finally, we use the Mish activation function and SIoU loss to increase the convergence speed and improve the accuracy simultaneously. The experimental results show that Light-YOLOv5 improves mAP by 3.3% compared to the original algorithm, reduces the number of parameters by 27.1%, decreases the computation by 19.1%, achieves FPS of 91.1. Even compared to the latest YOLOv7-tiny, the mAP of Light-YOLOv5 was 6.8% higher, which demonstrates the effectiveness of the algorithm