A new fire detection method based on flame color dispersion and similarity in consecutive frames

Wang, Teng; Shi, Lei; Yuan, Peng; Bu, Leping; Hou, Xinguo

doi:10.1109/cac.2017.8242754

Cited by 18 publications

(10 citation statements)

References 10 publications

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“…The results showed an accurate performance to detect fire with the presence of fire-like colored moving objects. Teng et al [36] used flame color dispersion and the similarity of consecutive frames in a region to detect fire in a video. In [37], spatial, motion, and temporal features are extracted from different regions to identify smoke and fire in IR (Infra-Red) videos.…”

Section: Feature-based Fire Detection and Segmentation Methodsmentioning

confidence: 99%

Wildfire Segmentation Using Deep Vision Transformers

Ghali

Akhloufi

Jmal³

et al. 2021

Remote Sensing

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In this paper, we address the problem of forest fires’ early detection and segmentation in order to predict their spread and help with fire fighting. Techniques based on Convolutional Networks are the most used and have proven to be efficient at solving such a problem. However, they remain limited in modeling the long-range relationship between objects in the image, due to the intrinsic locality of convolution operators. In order to overcome this drawback, Transformers, designed for sequence-to-sequence prediction, have emerged as alternative architectures. They have recently been used to determine the global dependencies between input and output sequences using the self-attention mechanism. In this context, we present in this work the very first study, which explores the potential of vision Transformers in the context of forest fire segmentation. Two vision-based Transformers are used, TransUNet and MedT. Thus, we design two frameworks based on the former image Transformers adapted to our complex, non-structured environment, which we evaluate using varying backbones and we optimize for forest fires’ segmentation. Extensive evaluations of both frameworks revealed a performance superior to current methods. The proposed approaches achieved a state-of-the-art performance with an F1-score of 97.7% for TransUNet architecture and 96.0% for MedT architecture. The analysis of the results showed that these models reduce fire pixels mis-classifications thanks to the extraction of both global and local features, which provide finer detection of the fire’s shape.

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Section: Feature-based Fire Detection and Segmentation Methodsmentioning

confidence: 99%

Wildfire Segmentation Using Deep Vision Transformers

Ghali

Akhloufi

Jmal³

et al. 2021

Remote Sensing

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“…A SVM based classifier is deployed with the feature space of 11 static attributes including colour distribution, texture parameters, and shape roundness, as well as 27 additional features computed with Fourier analysis to represent the temporal variations of colour, texture, roundness, area, and contour. Similarly, flame colour and motion dispersion and similarity in consecutive frames are also studied for fire detection [12,38].…”

Section: Preliminary Background On Visual Classificationmentioning

confidence: 99%

A novel image feature descriptor for SLM spattering pattern classification using a consumable camera

Han

2020

Int J Adv Manuf Technol

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In selective laser melting (SLM), spattering is an important phenomenon that is highly related to the quality of the manufactured parts. Characterisation and monitoring of spattering behaviours are highly valuable in understanding the manufacturing process and improving the manufacturing quality of SLM. This paper introduces a method of automatic visual classification to distinguish spattering characteristics of SLM processes in different manufacturing conditions. A compact feature descriptor is proposed to represent spattering patterns and its effectiveness is evaluated using real images captured in different conditions. The feature descriptor of this work combines information of spatter trajectory morphology, spatial distributions, and temporal information. The classification is performed using support vector machine (SVM) and random forests for testing and shows highly promising classification accuracy of about 97%. The advantages of this work include compactness for representation and semantic interpretability with the feature description. In addition, the qualities of manufacturing parts are mapped with spattering characteristics under different laser energy densities. Such a map table can be then used to define the desired spatter features, providing a non-contact monitoring solution for online anomaly detection. This work will lead to a further integration of real-time vision monitoring system for an online closed-loop prognostic system for SLM systems, in order to improve the performance in terms of manufacturing quality, power consumption, and fault detection.

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“…Celik and Demirel [5] proposed a generic rule-based flame pixel classification using the YCbCr color model to separate chrominance components from luminance ones. In addition, Wang [8] extracted the candidate fire area in an image using an HSI color model and calculated the dispersion of the flame color to determine the fire area. However, color-based fire detection methods are generally vulnerable to a variety of environmental factors such as lighting and shadow.…”

Section: Computer Vision-based Fire Detectionmentioning

confidence: 99%

A Video-Based Fire Detection Using Deep Learning Models

2019

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Fire is an abnormal event which can cause significant damage to lives and property. In this paper, we propose a deep learning-based fire detection method using a video sequence, which imitates the human fire detection process. The proposed method uses Faster Region-based Convolutional Neural Network (R-CNN) to detect the suspected regions of fire (SRoFs) and of non-fire based on their spatial features. Then, the summarized features within the bounding boxes in successive frames are accumulated by Long Short-Term Memory (LSTM) to classify whether there is a fire or not in a short-term period. The decisions for successive short-term periods are then combined in the majority voting for the final decision in a long-term period. In addition, the areas of both flame and smoke are calculated and their temporal changes are reported to interpret the dynamic fire behavior with the final fire decision. Experiments show that the proposed long-term video-based method can successfully improve the fire detection accuracy compared with the still image-based or short-term video-based method by reducing both the false detections and the misdetections.

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A new fire detection method based on flame color dispersion and similarity in consecutive frames

Cited by 18 publications

References 10 publications

Wildfire Segmentation Using Deep Vision Transformers

Wildfire Segmentation Using Deep Vision Transformers

A novel image feature descriptor for SLM spattering pattern classification using a consumable camera

A Video-Based Fire Detection Using Deep Learning Models

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