A Deep Normalization and Convolutional Neural Network for Image Smoke Detection

Yin, Zhijian; Wan, Boyang; Yuan, Feiniu; Xia, Xue; Shi, Jinting

doi:10.1109/access.2017.2747399

Cited by 233 publications

(111 citation statements)

References 15 publications

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“…To further illustrate the excellent performance of the proposed framework, we evaluated the framework using a public smoke dataset called the Yuan dataset [34]. The algorithms for the comparisons included MCCNN [1], DNCNN [34], ZF-Net [35], and HLTPMC [36].…”

Section: Results On the Yuan Datasetmentioning

confidence: 99%

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Zhao

Zhang

et al. 2019

Electronics

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Recently, many researchers have attempted to use convolutional neural networks (CNNs) for wildfire smoke detection. However, the application of CNNs in wildfire smoke detection still faces several issues, e.g., the high false-alarm rate of detection and the imbalance of training data. To address these issues, we propose a novel framework integrating conventional methods into CNN for wildfire smoke detection, which consisted of a candidate smoke region segmentation strategy and an advanced network architecture, namely wildfire smoke dilated DenseNet (WSDD-Net). Candidate smoke region segmentation removed the complex backgrounds of the wildfire smoke images. The proposed WSDD-Net achieved multi-scale feature extraction by combining dilated convolutions with dense block. In order to solve the problem of the dataset imbalance, an improved cross entropy loss function, namely balanced cross entropy (BCE), was used instead of the original cross entropy loss function in the training process. The proposed WSDD-Net was evaluated according to two smoke datasets, i.e., WS and Yuan, and achieved a high AR (99.20%) and a low FAR (0.24%). The experimental results demonstrated that the proposed framework had better detection capabilities under different negative sample interferences.

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Section: Results On the Yuan Datasetmentioning

confidence: 99%

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Zhao

Zhang

et al. 2019

Electronics

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“…Different fire signatures such a flame, smoke, and heat were used for fire and smoke detection using CNN by different researchers [20][21][22][23][24][25][26]. Some authors extended their work to include the detection of forest fires and enable fast response time for firefighting and the performance of rescue operations.…”

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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“…Smoke detection methods based on deep learning adopt the mainstream deep learning framework. In [18], the normalization and convolutional neural network (DNCNN) were applied to detect smoke in smoke video. In [19], a multichannel convolutional neural network was proposed to extract deep features of fire for fire detection.…”

Section: Introductionmentioning

confidence: 99%

Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness

Zhao¹,

Zhang²,

Zhang³

et al. 2020

Preprint

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Since smoke usually occurs before a flame arises, fire smoke detection is especially significant for early warning systems. In this paper, a DSATA(Depthwise Separability And Target Awareness) algorithm based on depthwise separability and target awareness is proposed. Existing deep learning methods with convolutional neural networks pretrained by abundant and vast datasets are always used to realize generic object recognition tasks. In the area of smoke detection, collecting large quantities of smoke data is a challenging task for small sample smoke objects. The basis is that the objects of interest can be arbitrary object classes with arbitrary forms. Thus, deep feature maps acquired by target-aware pretrained networks are used in modelling these objects of arbitrary forms to distinguish them from unpredictable and complex environments. In this paper, this scheme is introduced to deal with smoke detection. The depthwise separable method with a fixed convolution kernel replacing the training iterations can improve the speed of the algorithm to meet the enhanced requirements of real-time fire spreading for detecting speed. The experimental results demonstrate that the proposed algorithm can detect early smoke, is superior to the state-of-the-art methods in accuracy and speed, and can also realize real-time smoke detection.

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A Deep Normalization and Convolutional Neural Network for Image Smoke Detection

Cited by 233 publications

References 15 publications

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

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

Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness

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