BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.Comment: 8 pages, Proceedings of the 28th SIBGRAPI Conference on Graphics, Patterns and Images, IEEE Pres

Flame Boundary Measurement Using an Electrostatic Sensor Array

Flame boundary is an important geometrical characteristic for the evaluation of flame properties such as heat release rate and radiation. Reliable and accurate measurement of flame boundary is desirable for the prediction of flame structure and the optimization of combustion systems. Such measurement will inform the designers and operators of the combustion systems. This paper presents for the first time a study of using an electrostatic sensor array for flame boundary measurement. The electrostatic sensor is placed in the vicinity of the flame to sense its movement through charge transfer. The principle, design, implementation and assessment of a measurement system based on this methodology are introduced. Comparative experimental investigations with a digital camera conducted on a laboratory-scale combustion test rig show that the electrostatic sensor can respond to the variation of the distance between the electrode and the flame boundary. Reconstruction of the flame boundary is achieved using a set of distance measurements obtained from a sensor array. For diffusion flames over the range of fuel flow rate 0.60-0.80 L/min and premixed flames over the range of equivalence ratio 1.27-3.81, experimental results show that the measurement system is capable of providing reliable measurement of the flame boundary. The correlation coefficients under all test conditions are mostly larger than 0.96, the mean relative errors within 7.4% and the relative root mean square errors within 0.09. More accurate flame boundary measurements are achieved for diffusion flames. In addition, the overall polarity of charges in a flame can be determined from the polarity of the sensor signal

Efficient Deep CNN-Based Fire Detection and Localisation in Video Surveillance Applications

Convolutional neural networks (CNN) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this work, we propose an energy-friendly and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, the paper shows how a trade-off can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data

About Edge Detection in Digital Images

Edge detection is one of the most commonly used procedures in digital image processing. In the last 30-40 years, many methods and algorithms for edge detection have been proposed. This article presents an overview of edge detection methods, the methods are divided according to the applied basic principles. Next, the measures and image database used for edge detectors performance quantification are described. Ordinary users as well as authors proposing new edge detectors often use Matlab function without understanding it in details. Therefore, one chapter is devoted to some of Matlab function parameters that affect the final result. Finally, the latest trends in edge detection are listed. Picture Lena and two images from Berkeley segmentation data set (BSDS500) are used for edge detection methods comparison

Aeronautical Engineering: A continuing bibliography with indexes, supplement 185

This bibliography lists 462 reports, articles and other documents introduced into the NASA scientific and technical information system in February 1985. Aerodynamics, aeronautical engineering, aircraft design, aircraft stability and control, geophysics, social sciences, and space sciences are some of the areas covered

An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing

The determination of flame or fire edges is the process of identifying a boundary between the area where there is thermochemical reaction and those without. It is a precursor to image-based flame monitoring, early fire detection, fire evaluation, and the determination of flame and fire parameters. Several traditional edge-detection methods have been tested to identify flame edges, but the results achieved have been disappointing. Some research works related to flame and fire edge detection were reported for different applications; however, the methods do not emphasize the continuity and clarity of the flame and fire edges. A computing algorithm is thus proposed to define flame and fire edges clearly and continuously. The algorithm detects the coarse and superfluous edges in a flame/fire image first and then identifies the edges of the flame/fire and removes the irrelevant artifacts. The autoadaptive feature of the algorithm ensures that the primary symbolic flame/fire edges are identified for different scenarios. Experimental results for different flame images and video frames proved the effectiveness and robustness of the algorithm

Aeronautical Engineering: A continuing bibliography, supplement 132

This bibliography lists 342 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in January 1981