Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis

Pan, Hongyi; Badawi, Diaa; Çetin, A. Enis

doi:10.3390/s20102891

Cited by 57 publications

(41 citation statements)

References 31 publications

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“…Final fire regions were determined based on spatial and temporal features. Pan et al [23] proposed a camera-based wildfire detection system via transfer learning, in which block-based analysis strategy was used to improve fire detection accuracy. Redundant filters, which had low energy impulse response, were removed to ensure the model's efficiency on edge devices.…”

Section: Introductionmentioning

confidence: 99%

A Forest Fire Detection System Based on Ensemble Learning

Lin

et al. 2021

Forests

369

125

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Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.

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Section: Introductionmentioning

confidence: 99%

A Forest Fire Detection System Based on Ensemble Learning

Lin

et al. 2021

Forests

369

125

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“…In [ 16 ], a novel image fire detection algorithm based on the CNN models proposed in this study achieved an average precision accuracy of 83.7%. Furthermore, in [ 17 , 18 , 19 , 20 ], the CNN approach was applied to improve the performance of image fire detection technology. DL-based methods require significant training data, validation data, and test data.…”

Section: Related Workmentioning

confidence: 99%

An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems

Abdusalomov

Baratov

Kutlimuratov

et al. 2021

Sensors

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Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-speed fire detection using deep learning. A new special convolutional neural network was developed to detect fire regions using the existing YOLOv3 algorithm. Due to the fact that our real-time fire detector cameras were built on a Banana Pi M3 board, we adapted the YOLOv3 network to the board level. Firstly, we tested the latest versions of YOLO algorithms to select the appropriate algorithm and used it in our study for fire detection. The default versions of the YOLO approach have very low accuracy after training and testing in fire detection cases. We selected the YOLOv3 network to improve and use it for the successful detection and warning of fire disasters. By modifying the algorithm, we recorded the results of a rapid and high-precision detection of fire, during both day and night, irrespective of the shape and size. Another advantage is that the algorithm is capable of detecting fires that are 1 m long and 0.3 m wide at a distance of 50 m. Experimental results showed that the proposed method successfully detected fire candidate areas and achieved a seamless classification performance compared to other conventional fire detection frameworks.

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“…Therefore, the application of any normalizing technique might lead to the loss of the essential relationship between data points. Eventually, we employed sigmoid activation as the activation function, as presented in Equation (5), to indicate the probability of the evaluation result.…”

Section: Proposed Network Architecturementioning

confidence: 99%

Automatic Fire and Smoke Detection Method for Surveillance Systems Based on Dilated CNNs

2020

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The technologies underlying fire and smoke detection systems play a crucial role in ensuring and delivering optimal performance in modern surveillance environments. In fact, fire can cause significant damage to lives and properties. Considering that the majority of cities have already installed camera-monitoring systems, this encouraged us to take advantage of the availability of these systems to develop cost-effective vision detection methods. However, this is a complex vision detection task from the perspective of deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a new method based on a deep learning approach, which uses a convolutional neural network that employs dilated convolutions. We evaluated our method by training and testing it on our custom-built dataset, which consists of images of fire and smoke that we collected from the internet and labeled manually. The performance of our method was compared with that of methods based on well-known state-of-the-art architectures. Our experimental results indicate that the classification performance and complexity of our method are superior. In addition, our method is designed to be well generalized for unseen data, which offers effective generalization and reduces the number of false alarms.

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Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis

Cited by 57 publications

References 31 publications

A Forest Fire Detection System Based on Ensemble Learning

A Forest Fire Detection System Based on Ensemble Learning

An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems

Automatic Fire and Smoke Detection Method for Surveillance Systems Based on Dilated CNNs

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