Candidate Fire Regions Research Articles

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

FireAnalyst: An effective system for detecting fire geolocation and fire behavior in forests using mathematical modeling

This study proposes an effective new fire detection method and monitoring software for an early-warning fire detection system aimed at valuable forested areas, such as botanical parks or high conservation value forests, particularly those with boundaries. These critical forested areas need to be appropriately managed because they contain large concentrations of biological diversity, including threatened or endangered species, which are very susceptible to fire outbreaks; thus, early detection of fire and rapid response have a very important place in the fight against fire in those areas. In this proposed system, special detectors with state-of-the-art multispectral infrared technology and mathematical modeling algorithms have been utilized to create a smart fire detection system that can detect fires at a very early stage. The geolocation and behavior of emerging fires in a forest are also estimated with maximum spatial resolution by superimposing the detection areas of multispectral infrared detectors. In this study, candidate fire regions are examined for feasibility first. Next, the most suitable fire detector type is determined and used for expanding the fire control area, to have the highest positional accuracy in estimating the location of an emerged fire. Thereafter, mathematical models for the positioning of the detectors are created to have high spatial resolution in detecting the coordinates of forest fires by using the libraries of Google Maps APIs in the cloud. The geolocation of the fire and behavior of the fire inside the model are then simulated visually on the map portal, thanks to an extraordinary standalone software program called FireAnalyst. The proposed system was implemented for the Faruk Yalçın Zoo and Botanical Park in Darıca, Turkey. Experimental results have indicated that monitoring fires with FireAnalyst using selected multispectral infrared detectors positioned toward the center geometry outperformed other fire monitoring systems, providing a significantly shortened fire detection timeframe and high spatial resolution (up to 4.5 m) in detecting the geolocation of a fire in a minimum of ~3599.56 m2 forested area, and it adds functionality, such as real-time fire behavior analysis (spreading speed of fire, spreading direction).

Two-Step Cascading Algorithm for Camera-based Night Fire Detection

In this paper, we propose a new fire monitoring system that automatically detect fire flames in night-time using a CCD camera. The proposed system consists of two cascading steps to reliably detect fire regions. First, ELASTIC-YOLOv3 is proposed to better detect a small fires. The main role of ELASTIC-YOLOv3 is to find fire candidate regions in images as the first step. The candidate fire regions are passed to the second verification step to detect more reliable fire region results. The second step takes into account the dynamic characteristic of the fire. To do this, we construct fire-tubes by connecting the fire candidate regions detected in several frames, and extract the histogram of optical flow (HOF) from the fire-tube. However, because the extracted HOF feature vector has a considerably large size, the feature vector is reduced by applying a predefined bag of feature (BOF) and then applied to the fast random forest classifier to verify the final fire regions instead of heavy recurrent neural network (RNN). The proposed method has been experimentally shown a faster processing time and higher fire detection accuracy with lower missing and false alarm.

Fire detection for video surveillance applications using ICA K-medoids-based color model and efficient spatio-temporal visual features

Automated detection of fire flames in videos shot from a surveillance camera is an active research topic, as fire detection must be accurate and fast. The present study proposes and evaluates an efficient fire detection method. The contributions of this method lies in threefold: (1) a robust ICA (Imperialist Competitive Algorithm) K-medoids-based color model first is developed to reliably detect all candidate fire regions in a scene; (2) a motion-intensity-aware motion detection technique is introduced to simply extract the regions containing movement together with the motion intensity rate of every moving pixel, which are then used to analyze the characteristics of the fire; (3) a set of new spatio-temporal features having the distinct characteristics of fire flames are extracted from the candidate fire regions which are fed into a support vector machine classifier in order to distinguish real fire regions from non-real ones. The experimental results for a set of benchmark fire video datasets and videos provided in this research confirm that the proposed method outperforms state-of-the-art fire detection approaches, providing high detection accuracy and a low false detection rate.

Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection

Every year, a large number of wildfires all over the world burn forested lands, causing adverse ecological, economic, and social impacts. Beyond taking precautionary measures, early warning and immediate response are the only ways to avoid great losses. To this end, in this paper we propose a computer vision approach for fire-flame detection to be used by an early-warning fire monitoring system. Initially, candidate fire regions in a frame are defined using background subtraction and color analysis based on a nonparametric model. Subsequently, the fire behavior is modeled by employing various spatio-temporal features, such as color probability, flickering, spatial, and spatio-temporal energy, while dynamic texture analysis is applied in each candidate region using linear dynamical systems and a bag-of-systems approach. To increase the robustness of the algorithm, the spatio-temporal consistency energy of each candidate fire region is estimated by exploiting prior knowledge about the possible existence of fire in neighboring blocks from the current and previous video frames. As a final step, a two-class support vector machine classifier is used to classify the candidate regions. Experimental results have shown that the proposed method outperforms existing state-of-the-art algorithms.

Fire detection and 3D surface reconstruction based on stereoscopic pictures and probabilistic fuzzy logic

In this paper, a new fire detection method is proposed, which is based on using a stereo camera to calculate the distance between the camera and the fire region and to reconstruct the 3D surface of the fire front. For the purpose of fire detection, candidate fire regions are identified using generic color models and a simple background difference model. Gaussian membership functions (GMFs) for the shape, size, and motion variation of the fire are then generated, because fire regions in successive frames change constantly. These three GMFs are then applied to fuzzy logic for real-time fire verification. After segmentation of the fire regions from left and right images, feature points are extracted using a matching algorithm and their disparities are computed for distance estimation and 3D surface reconstruction. Our proposed algorithm was successfully applied to a fire video dataset and its detection performance was shown to be better than that of other methods. In addition, the distance estimation method yielded reasonable results when the fire was a short distance from the camera and the reconstruction of the 3D surface showed a shape that was almost the same as that of the real fire.

Fire flame detection in video sequences using multi-stage pattern recognition techniques

In this paper, we propose an effective technique that is used to automatically detect fire in video images. The proposed algorithm is composed of four stages: (1) an adaptive Gaussian mixture model to detect moving regions, (2) a fuzzy c-means (FCM) algorithm to segment the candidate fire regions from these moving regions based on the color of fire, (3) special parameters extracted based on the tempo-spatial characteristics of fire regions, and (4) a support vector machine (SVM) algorithm using these special parameters to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate.

An Improved Probabilistic Approach for Fire Detection in Videos

This paper proposes an improved probabilistic approach using two improved feature representations. These features are color and motion. First, an improved probabilistic model for color-based fire detection is proposed, and candidate fire regions are generated from this model. Then, an improved motion feature is used for final decision. The performance of the proposed approach showed about 0.2758 accuracy in false positive rate, and 0.2636 accuracy in false negative rate on a benchmark fire video database, which represents a decrease of 46.6% in false positive rate, and a decrease of 52.1% in false negative rate compared to the probabilistic approach.

Hybrid fire detection using hidden Markov model and luminance map

Recently, fire detection is a hot research topic. Although many detection methods have been proposed, there exist high false alarms because of the interference of fire-colored moving object in the complex environments. In this paper, a hybrid method is proposed. First, we get the set of candidate fire regions. Then these candidate fire regions are analyzed to exclude the fire-colored moving object. Our contributions are using the hidden Markov model (HMM) based on spatio-temporal feature and the variance of luminance map motivated by visual attention, and combining both for fire detection. The wrong detection can be reduced greatly. Experiment results show our proposed method has a good performance and it is robust to be used in complex environment compared with previous algorithms.

Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks

This paper proposes a new vision-based early fire detection method for real-world application. First, candidate fire regions are detected using a background and color model of fire. Probabilistic models of the fire are then generated based on the fact that fire pixel values in consecutive frames change constantly. These models are then applied to Bayesian Networks. This paper uses hierarchical Bayesian Networks that contain intermediate nodes. Four probability density functions for evidence at each node are used. The probability density functions for each node are modeled using the skewness of the color red, and three high frequencies obtained from a wavelet transform. The proposed system was successfully applied to various fire-detection tasks in real-world environments, and it effectively distinguished fire from fire-colored moving objects.

Fire detection based on vision sensor and support vector machines

This paper proposes a new vision sensor-based fire-detection method for an early-warning fire-monitoring system. First, candidate fire regions are detected using modified versions of previous related methods, such as the detection of moving regions and fire-colored pixels. Next, since fire regions generally have a higher luminance contrast than neighboring regions, a luminance map is made and used to remove non-fire pixels. Thereafter, a temporal fire model with wavelet coefficients is created and applied to a two-class support vector machines (SVM) classifier with a radial basis function (RBF) kernel. The SVM classifier is then used for the final fire-pixel verification. Experimental results showed that the proposed approach was more robust to noise, such as smoke, and subtle differences between consecutive frames when compared with the other method.