A Wave-Shaped Deep Neural Network for Smoke Density Estimation

Yuan, Feiniu; Zhang, Lin; Xia, Xue; Huang, Qinghua; Li, Xuelong

doi:10.1109/tip.2019.2946126

Cited by 74 publications

(30 citation statements)

References 38 publications

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“…Yuan et al [45] proposed an end-to-end segmentation network that fuses dual-branch features for blurred, semi-transparent, and nonrigid boundaries of smoke targets, which outputs a soft segmentation probability map with 0-1 continuous values and gains pixel-by-pixel density estimation. Yuan et al [61] believed that the full fusion of information between the high and low layers of the codec could improve the segmentation accuracy of fuzzy objects such as smoke and clouds and proposed a deep neural network with a wave structure using a synthetic smoke dataset for training to achieve smoke density estimation. Yuan et al [62] proposed a classification-assisted gated regression semantic segmentation network for the problem of inter-class similarity of smoke and small smoke segmentation, which can learn long-distance feature relationships and contextual information and improve the accuracy of smoke segmentation.…”

Section: B Deep-learning-based Smoke Segmentation Methodsmentioning

confidence: 99%

AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network

Wang

Hong

Han

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Smoke is more observable than open fires. Optical satellite video has the advantages of a wide monitoring range, fast response speed, and good economy in large-scale surface smoke monitoring tasks. It can be used in wide-area forest wildfire monitoring, battlefield dynamic monitoring, disaster relief decision-making. The smoke segmentation method based on traditional handcrafted features is easily limited by the scene and data. This paper introduces the deep learning method to the optical satellite video smoke target segmentation. However, due to the lack of real smoke images and the blurred edges of smoke, there are currently few labeled datasets for smoke segmentation in high-resolution optical satellite imagery scenes, which cannot provide sufficient training data for deep learning models. The smoke image from the satellite perspective also has the characteristics of multi-scale features and ground object background interference. To solve the above problems, we construct a set of high-resolution optical satellite imagery smoke synthesis datasets based on the optical imaging process of smoke targets, which saves the cost of manual labeling. In addition, we design an attention-guided optical satellite video smoke segmentation network model (AOSVSSNet), which can effectively suppress the ground object background's false alarm and extract the smoke's multi-scale features. Synthetic data faces the transferability problem in real-world applications, so the physical constraints of the smoke imaging process are introduced into the loss function to improve the generalization of the model in real smoke data. The comprehensive evaluation results show that the method outperforms representative semantic segmentation networks.

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Section: B Deep-learning-based Smoke Segmentation Methodsmentioning

confidence: 99%

AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network

Wang

Hong

Han

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…UMN and PETS2009 datasets were used to performed experiments. Yuan et al [ 24 ] proposed a wave-shaped neural network (W-Net) to label the density of smoke in images, which is difficult task, so virtual dataset was created. Convolutional encoder decoder architectures were assembled to maximize the input for information extraction from smoke density images and W-Net was proposed.…”

Section: Related Workmentioning

confidence: 99%

Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Aamir

Ali

Irfan

et al. 2021

Sensors

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Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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“…With the rapid development of artificial intelligent (AI) models [25], many researches applied AI models to detect abnormal conditions for mechanical and civil engineering [26][27][28][29][30][31] as well as fire detection domain [32][33][34][35][36][37][38][39]. Recently, Yuan et al proposed a smoke density estimation network.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Yuan et al proposed a smoke density estimation network. In order to encoding abundant semantic information, a stacked convolutional encoder-decoder structure was designed to estimate smoke density from flame images and real videos [32]. Muhammad et al utilized an energyfriendly and computationally efficient CNN architecture for fire detection, localization, and semantic understanding of the scene of the fire [33].…”

Section: Introductionmentioning

confidence: 99%

Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network

2021

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Video flame and smoke-based fire detection usually exhibit large variations in the feature of color, texture, shapes, etc., caused by the complex environment. It is difficult to develop a robust method to detect fire based on single or multiple fire features. Since convolutional neural network (CNN) has reported state-of-the-art performance in a wide range of fields. This study present a method based on SLIC-DBSCAN and convolutional neural network to recognize flame and smoke modes connected to fire stages. First, simple linear iterative clustering (SLIC) is acted as the pre-processing step to over segment images into super-pixels. Then the use of density based spatial clustering of application with noise (DBSCAN) gathered the similar super-pixels into several clusters, which in turn provide better smoke detection accuracy by using CNN. Comparison studies are performed to base on smoke image from publicly available data and self-collected data. The experimental results demonstrated the improved smoke detection capabilities by the present method.

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A Wave-Shaped Deep Neural Network for Smoke Density Estimation

Cited by 74 publications

References 38 publications

AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network

AOSVSSNet: Attention-Guided Optical Satellite Video Smoke Segmentation Network

Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network

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