Deep Convolutional Neural Networks for Fire Detection in Images

Sharma, Jivitesh; Granmo, Ole‐Christoffer; Goodwin, Morten; Fidje, Jahn Thomas

doi:10.1007/978-3-319-65172-9_16

Cited by 180 publications

(107 citation statements)

References 15 publications

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“…In disaster detection and categorization studies, researchers have started to employ DL to detect wildfires (Lee et al, ; Sharma et al, ; Q. Zhang, Xu, et al, ) and landslides from remote sensing images (Ying Liu & Wu, ). Liu and Wu () applied preprocessing steps including discrete wavelet transformation and noise corruption and trained an SDAE to identify landslides on the transformed image.…”

Section: Transdisciplinary Applications Of DL and Its Interpretationmentioning

confidence: 99%

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Shen

2018

Water Resources Research

760

411

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Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem‐relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image‐like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience, where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation‐based analysis, inversion of network‐extracted features, reduced‐order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem‐specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

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Section: Transdisciplinary Applications Of DL and Its Interpretationmentioning

confidence: 99%

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Shen

2018

Water Resources Research

760

411

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“…After the initial interest, investigation on deeper models began to take place with [2], where smaller stacked kernels started to be investigated and advanced with Inception [3] and ResNet [4] architectures. As far as security and safety domains are concerned we can encounter flood classification in [5], [6] and fire classification in [7], [8].…”

Section: Related Workmentioning

confidence: 99%

People and Vehicles in Danger - A Fire and Flood Detection System in Social Media

Giannakeris

Avgerinakis

Karakostas

et al. 2018

2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

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Abstract-This paper presents a novel warning system framework for detecting people and vehicles in danger. The system was tested in several images compiled from Flickr and other social media sources and is highly suggested to get integrated in future warning surveillance and safety systems for preventing or solving crisis events. The proposed framework recruits State-ofthe-Art deep learning technologies so as to solve a series of image processing and machine learning challenges and provides a near real-time localization solution for detecting and scoring severity safety levels of people and vehicles in flood and fire images.

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“…For transfer learning established networks are used, namely VGG16 [22] and ResNet50 [6] which have also been used in prior works outlined in Section 2.2 such as [10,21,11] as well as networks more suited to embedded domains such as MobileNet [9]. The feature extraction part is frozen for each of these networks, applying all necessary preprocessing steps to the input image, and add a classification layer on top similar to prior works.…”

Section: Transfer Learning Networkmentioning

confidence: 99%

Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles

Kyrkou

Theocharides

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

113

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Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this paper focuses on the automated aerial scene classification of disaster events from on-board a UAV. Specifically, a dedicated Aerial Image Database for Emergency Response (AIDER) applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ∼ 3× higher performance compared to existing models with minimal memory requirements with less than 2% accuracy drop compared to the state-ofthe-art. These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.

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Deep Convolutional Neural Networks for Fire Detection in Images

Cited by 180 publications

References 15 publications

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

People and Vehicles in Danger - A Fire and Flood Detection System in Social Media

Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles

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