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The purpose of this paper is to present a deep ensemble neural network model for the detection of forest fires in aerial vehicle videos.

Presented deep ensemble models include four convolutional neural networks (CNNs): a faster region-based CNN (Faster R-CNN), a simple one-stage object detector (RetinaNet) and two different versions of the you only look once (Yolo) models. The presented method generates its output by fusing the outputs of these different deep learning (DL) models.

The presented fusing approach significantly improves the detection accuracy of fire incidents in the input data.

The computational complexity of the proposed method which is based on combining four different DL models is relatively higher than that of using each of these models individually. On the other hand, however, the performance of the proposed approach is considerably higher than that of any of the four DL models.

The simulation results show that using an ensemble model is quite useful for the precise detection of forest fires in real time through aerial vehicle videos or images.

By this method, forest fires can be detected more efficiently and precisely. Because forests are crucial breathing resources of the earth and a shelter for many living creatures, the social impact of the method can be considered to be very high.

This study fuses the outputs of different DL models into an ensemble model. Hence, the ensemble model provides more potent and beneficial results than any of the single models.

This paper aims to describe the sensors used in fire detection products and systems.

Following an introduction, this paper discusses the sensors used for fire detection according to the variables measured. It considers the sensing of smoke, temperature, flames and combustion products and covers residential and industrial uses, together with a short discussion of wildfire detection methods.

This shows that a multitude of different sensing techniques are used to detect fires and no single method is universally applicable. This reflects both the differing types of fires that can occur and also the many applications and environments in which fire detectors and systems are deployed.

This paper provides details of the sensor technologies and techniques used to detect fires in residential, industrial and natural environments.

Due to the environmental conditions, the detection and identification of hazardous situations in coal mines is a challenge. The purpose of this research is to focus on the underground fire detection, especially of smoldering fires, which are characterized by the outgassing of CO and C2H4.

The study developed a system based on a single semiconductor gas sensor and sensors for relative humidity and temperature. With a high‐dynamic‐range hardware control and data acquisition platform a commercial semiconductor gas sensor is operated with an application‐optimized temperature cycle to improve stability and selectivity.

A hierarchical evaluation strategy not only allows identification of smoldering fires signified by CO and C2H4 with a ratio of 100:1, but is also suitable for separating or quantifying typical interfering compounds such as CH4, CO, NOX or H2 thus helping to avoid costly false alarms. After promising laboratory pre‐tests, a system was built for field tests including test gases and re‐calibration algorithms. Currently the authors are working on a self‐monitoring strategy based on redundant data from impedance spectroscopy to improve the stability of the system.

This approach allows detection of C2H4 at sub‐ppm concentrations even in the presence of CH4 at levels up to 1 percent with only a single gas sensor. The system achieves classification reproducibility as well as robustness allowing the development of a cost‐efficient under‐ground fire detection system. The novel self‐monitoring strategy will further improve the reliability of the system.

The purpose of this paper is to develop an early fire-alarm raising system based on video processing, and combine it with the omnidirectional projecting system. It not only gives alarm immediately in early fire so that people can be able to strive for more time to escape from the spot, but also solves problem of discontinued screen which was presented fire scene.

The smoke detection system is made by image processing. The flowchart of smoke detection is improved, which the method of background updating can filter out the moving objects that only stay for a short time in the image; and avoids these objects being determined to be the background. Moreover, the authors extract the flickering area to separate the non-smoke and smoke from the candidate of smoke regions. Finally, the image processing is applied in omnidirectional projecting system, then presented the 360-degree fire scene.

The results show that the smoke detection system can accurately detect the smoke and mark its location, then combining it with the omnidirectional projecting system, although the resolution of omnidirectional projecting system is not enough, it can present a continued screen and location of smoke on the 360-degree cylindrical screen.

This paper develops the smoke detection based on a improved method of image processing, and the control center staff can see the 360-degree fire scene via omnidirectional projecting system, so shorten the time to find the source of smoke.

Fire is a common disaster. Even though simple sensors such as those detecting smoke or heat are popularly employed, they require close proximity to fire. In order to obtain more reliable and more complete information, fire detection by vision sensing has recently acquired increasing attention. In the vision-based fire sensing, colour is usually used as an important cue for flame detection. However, considering there are still a large number of black-and-white (B/W) CCTV cameras installed for security purposes, a technique that can detect flame reliably in grey-scale images will be useful to protect human lives and property from the fire disaster. The paper aims to discuss these issues.

This article describes the automatic detection of fire flames in the grey-scale image sequences by a two-level image processing scheme: pixel-level and frame-level. In pixel-level processing, an evaluation function is devised to extract pixels that possibly belong to the flame region, particularly to its boundaries. Extracted fire pixel candidates are verified in frame-level processing by monitoring their distribution variations in sequential images. A circle is fitted to the candidate pixels in each image for efficient monitoring, and the presence of flame is reasoned when the position and size of the circle increase with high fluctuations.

Experimental results show that the proposed method can detect flame quite reliably using the intensity information and its temporal variations in grey-scale image sequences.

This paper presents a novel technique of vision-based flame detection. Unlike most existing techniques, the proposed technique is based on the grey-scale images of a B/W camera. To the best of the author's knowledge, it may be the first of its kind developed for general application to indoor and outdoor scenes.

NOW that the first two prototype Aerospatiale/British Aircraft Corporation Concorde aircraft 001 and 002 have successfully completed their initial flight phase, the age of supersonic commercial transport airliners is almost upon us.

Fire protection has several components: prevention, containment, detection, and suppression. While building codes and inspectors generally do not require special construction techniques or fire protection systems for computer rooms, economic and service factors may dictate that special protection be given such facilities. This article discusses emergency planning, the various types of fire detection and suppression systems, and future options, with particular attention given to halon and possiblehalon‐replacements.

Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.

This paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.

Based on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.

Aiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

The objectives of this paper are to review current legislation and guidelines on fire safety in library facilities, and to present the finding of a case study to assess the provision of these requirements in the main library of King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia.

The authors have analyzed published literature and identified characteristics of combustible information media in libraries, causes of fire in library facilities, basic requirements of fire detection systems, basic requirements of fire suppression systems, and basic requirements of egress and evacuation. The authors then carried out an investigation to assess the provision of these requirements in the main library of King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

The findings from the case study conducted in the paper indicated that although the main library at KFUPM was found to have sufficient fire detection system, fire suppression system, and provision of the basic requirements of egress and evacuation, there are some recommendations for improving the status of fire safety. These recommendations are summarized as follows: a floor plan should be posted on each floor to illustrate the locations of evacuation routes, exits, and other fire safety devices; signs should be installed in the third and the fourth floor to guide students to evacuation exits; and furniture should be removed from the area in front of the stairway in the third floor.

Fires are one of the main causes of life and property loss in cultural buildings. The high fire load of libraries makes them particularly susceptible to the spread of fire. The paper provides a systematic approach to the assessment of the provision of fire safety requirements in existing library facilities. The paper also provides insights to facility managers responsible for day‐to‐day operations of library and/or archival facilities.

The purpose of this paper is to investigate a set of preventive measures required for mitigating fire risks in big box retail facilities.

The paper identifies the potential sources of ignition and fuel in big box retail facilities. It describes the variety of hazardous situations commonly found in such facilities worldwide. The paper then endeavors to discuss the series of fire protection challenges that could be faced during fire emergencies. It also explores the challenges of evacuation and rescue in such mega store facilities.

Mega stores, distribution centers and large retail stores are amongst the most challenging occupancies from a fire protection perspective. Fires can occur in big box retail facilities at any time and from a number of causes. These facilities represent a type of occupancy that poses considerable challenges to both fixed fire suppression systems and fire departments in cases of fire emergencies. The paper also describes the responsibilities of building management staff towards their employees and the public. Facility managers should always seek proactive measures to reduce the risk of fire and fire spread in big box retail facilities. These measure include providing sufficient number and capacity of exits; clear exit access; efficient smoke detection systems; voice communication systems; and efficient automatic sprinkler systems.

This paper serves to increase the awareness about fire and its effects in mega store facilities. The paper provides practical value to property directors and facility managers responsible for the daily operations of mega store facilities and for surveyors inspecting such properties.