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

Muhammad, Khan; Ahmad, Jamil; Lv, Zhihan; Bellavista, Paolo; Yang, Po

doi:10.1109/tsmc.2018.2830099

Cited by 344 publications

(165 citation statements)

References 42 publications

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“…Muhammad et al [25] used a lightweight CNN based on the SqueezeNet [26] architecture for fire detection, localization, and semantic understanding of a fire scene. Muhammad et al [27] also replaced SqueezeNet with GoogleNet [28] for fire detection using a CCTV surveillance network.…”

Section: Machine Learning and Deep Learning-based Fire Detectionmentioning

confidence: 99%

Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Park

2020

Sensors

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While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy.

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Section: Machine Learning and Deep Learning-based Fire Detectionmentioning

confidence: 99%

Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Park

2020

Sensors

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“…In their method, the full image is first tested by the global image-level classifier, and if a fire is detected, then a fine-grained patch classifier is used for precisely localizing the fire patches. Muhammad et al [14] proposed a fire surveillance system based on a fine-tuned CNN fire detector. This architecture is an efficient CNN architecture for fire detection, localization, and semantic understanding of the scene of the fire inspired by the Squeeze Net [15] architecture.…”

Section: Deep Learning-based Approachmentioning

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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“…In the convolution operation, feature maps are generated by applying kernels of different sizes to the input data. A pooling operation is performed on the feature maps with maximum activations from small neighbourhood in the feature maps, as published by Muhammed et al [18,36]. e pooling operation handles the computational overhead of passing the features extracted directly to the classifier, as proposed by Aloysius and Geetha in [32].…”

Section: Convolutional Neural Networkmentioning

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

Abstract: This is a repository copy of Efficient deep CNN-based fire detection and localization in video surveillance applications.

Cited by 344 publications

References 42 publications

Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

A Video-Based Fire Detection Using Deep Learning Models

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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