Deep Convolutional Neural Networks for Forest Fire Detection

Zhang, Qingjie; Xu, Jiaolong; Xu, Liang; Guo, Haifeng

doi:10.2991/ifmeita-16.2016.105

Cited by 153 publications

(79 citation statements)

References 14 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

655

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

655

411

View full text Add to dashboard Cite

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“…This approach was proven to achieve a better classification performance than some other relevant conventional fire detection methods. Zhang et al [24] proposed a joined deep CNN. With this method, the fire detection is applied in a cascaded fashion; thus, the full image is first tested using the global image-level classifier, and if a fire is detected, the fine-grained patch classifier is followed to detect the precise location of the fire patches.…”

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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“…Sebastien [12] proposed a fire detection network based on CNN where the features are simultaneously learned with a Multilayer Perceptron (MLP)-type neural net classifier by training. Zhang et al [13] also proposed a CNN-based fire detection method which is operated in a cascaded fashion. 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.…”

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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Deep Convolutional Neural Networks for Forest Fire Detection

Cited by 153 publications

References 14 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

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

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