Efficient Fire Detection for Uncertain Surveillance Environment

Muhammad, Khan; Khan, Salman; Elhoseny, Mohamed; Shah, Syed Hassan Ahmed; Baik, Sung Wook

doi:10.1109/tii.2019.2897594

Cited by 234 publications

(119 citation statements)

References 39 publications

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“…We have compared the performances of our scheme in terms of three metrics including false positive, false negative, and accuracy. While the method of Khan Muhammad et al [31] performs the best in terms of false positive and false negative, ours with the delayed decision of the majority voting in 10 s outperforms in accuracy. Note that our proposed method can produce this better by introducing the delayed decision in DTA.…”

Section: Majority Voting and Interpretation Of Fire Behaviormentioning

confidence: 65%

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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Section: Majority Voting and Interpretation Of Fire Behaviormentioning

confidence: 65%

A Video-Based Fire Detection Using Deep Learning Models

2019

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“…The disadvantage of these methods is that the neural network models require high-configuration hardware and are unsuitable for embedded systems. SqueezeNet and MobileNetV2 were proposed to meet the constraints of embedded systems [36,37]. At the cost of lower accuracy, these lightweight neural network models are computationally efficient and can be deployed to platforms like Raspberry Pi.…”

Section: Fire Detection Via Machine Learning and Deep Learning Methodsmentioning

confidence: 99%

Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

Tang

Liu

Chen

et al. 2020

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Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

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“…Muhammad et al [17,18] proposed a cost-effective fire detection CNN architecture for surveillance videos with less computational time and memory footprints. eir model is inspired by GoogLeNet architecture, considering its reasonable computational complexity and suitability for the intended problem compared with other computationally expensive networks such as AlexNet.…”

Section: Literature Reviewmentioning

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 Fire Detection for Uncertain Surveillance Environment

Cited by 234 publications

References 39 publications

A Video-Based Fire Detection Using Deep Learning Models

A Video-Based Fire Detection Using Deep Learning Models

Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

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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