Real-time fire detection method combining AdaBoost, LBP and convolutional neural network in video sequence

Maksymiv, Oleksii; Rak, Taras; Peleshko, Dmytro

doi:10.1109/cadsm.2017.7916148

Cited by 31 publications

(11 citation statements)

References 7 publications

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“…In the third case study, the effect of changing in size of training set on the recognition rate was observed. There were 10,15,20,25,30,35 and 40 images of each state randomly selected from the RTS transformation set as the training sets. Accordingly, the rest of images transformed by RTS composed seven corresponding testing sets.…”

Section: Results For the Third Case Study And Comparisonmentioning

confidence: 99%

“…Wang et al [9] proposed a fire smoke detection algorithm based on optical flow method and texture feature, which could be used for early fire alarm. Several researchers also have theoretically investigated that the local binary pattern (LBP) [10,11], Wald-Wolfowitz random test algorithm [12], and convolutional neural network [13] were suitable for flame image detection. Unfortunately, just like the above, most of the existing methods only aim to indicate whether a frame contains flame or not, rather than the combustion state of flame.…”

Section: Introductionmentioning

confidence: 99%

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Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

et al. 2018

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Accurate combustion state recognition of flame images not only plays an important role in social security, but also contributes to increasing thermal efficiency and product quality. To improve the accuracy of feature extraction and achieve the combustion state recognition, a novel method based on radial Chebyshev moment invariants (RCMIs) and an improved firefly algorithm-wavelet support vector machine (IFA-WSVM) model is proposed. Firstly, the potential flame pixels and the potential flame contour are obtained in the pre-processing phase. Then, the rotation, translation and scaling (RTS) invariants of radial Chebyshev moments are derived. Combing the region and contour moments, the RCMIs of pre-processed and edge images are calculated to construct multi-feature vectors. To enhance the recognition performance, an IFA-WSVM model is built, where the IFA is applied to search the best parameters of WSVM. Then, the IFA-WSVM model is used to recognize the combustion state. Finally, the result for case studies show that the proposed method is superior to methods based on HMIs and ZMIs, achieving the highest rate of 99.07% in real time. The IFA algorithm also outperforms other benchmark algorithms. Even for the images transformed by RTS and small size of training sets, the proposed method continues to exhibit the best performance.

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Section: Results For the Third Case Study And Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

et al. 2018

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“…Yin et al [20] proposed a deep normalization and convolutional neural network (DNCNN), which improved the speed and accuracy of smoke detection by replacing traditional convolutional layers with normalization and convolutional layers. Maksymiv et al [21] used Local Binary Pattern (LBP) and the AdaBoost algorithm to obtain the region where flames may exist in the image, and then a CNN is used to discriminate each region. Both methods can obtain the locations of the flames, but the limitation is that the locations are not accurate.…”

Section: Related Workmentioning

confidence: 99%

Information-Guided Flame Detection Based on Faster R-CNN

2020

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Due to the diversity of the shape and texture of flame, and interference objects that similar to flame in color, detecting the position of flame from images is a difficult task. To enable generic object detection methods to achieve better performance in flame detection tasks, a color-guided anchoring strategy is proposed that uses color features of the flame to limit the location of the anchor. To solve the problem of high false alarm rate when directly using generic object detection methods in flame detection, a global information-guided flame detection method is proposed, this strategy uses a parallel network to generate image global information. We use these two methods to improve Faster R-CNN (Regions with Convolutional Neural Network features) to perform the fire detection process in a guided manner. Experiments on the BoWFire dataset show that our method improved detection speed by 10.1% compared with the original Faster R-CNN. In addition, the false alarm rate is decreased by 21.5%, and the overall accuracy of detection is increased by 9.3%. Experiments on PascalVOC and Corsician datasets further demonstrate the robustness of the proposed methods.

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“…A real-time fire detection method combining AdaBoost, local binary pattern (LBP), and a convolutional neural network in video sequence was produced by Maksymiv et al in [28]. e proposed framework for emergency detection consisted of two main parts; the first part generates regions or areas where flame or smoke may be present in the video stream.…”

Section: Literature Reviewmentioning

confidence: 99%

“…e sensors used, respectively, in [28][29][30][31] were chosen based on detection range, size, and cost. By adopting a multisensor approach, the need to fuse the data arises.…”

Section: System Design and Developmentmentioning

confidence: 99%

Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

Sowah

Apeadu

Gatsi

et al. 2020

Journal of Engineering

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This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

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Real-time fire detection method combining AdaBoost, LBP and convolutional neural network in video sequence

Cited by 31 publications

References 7 publications

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

Information-Guided Flame Detection Based on Faster R-CNN

Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

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