Wildfire Segmentation Using Deep Vision Transformers

Ghali, Rafik; Akhloufi, Moulay A.; Jmal, Marwa; Mseddi, Wided Souidene; Attia, Rabah

doi:10.3390/rs13173527

Cited by 65 publications

(39 citation statements)

References 78 publications

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“…It was able to distinguish fire and background when the image consisted of smoke and in different weather conditions. Similarly to other work, it was able to locate smaller fire patches [7]. The downside to the large model is the long training time and being computationally heavy.…”

Section: Wildfire Segmentation Using Deep Vision Transformersmentioning

confidence: 72%

Using neural networks to detect fire from overhead images

Kurasinski

Tan

Malekian

2022

Preprint

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The use of artificial intelligence (AI) is increasing in our everyday applications. One emerging field within AI is image recognition. Research that has been devoted to predicting fires involves predicting its behaviour. That is, how the fire will spread based on environmental key factors such as moisture, weather condition, and human presence. The result of correctly predicting fire spread can help firefighters to minimise the damage, deciding on possible actions, as well as allocating personnel effectively in potentially fire prone areas to extinguish fires quickly. Using neural networks (NN) for active fire detection has proven to be exceptional in classifying smoke and being able to separate it from similar patterns such as clouds, ground, dust, and ocean. Recent advances in fire detection using NN has proved that aerial imagery including drones as well as satellites has provided great results in detecting and classifying fires. These systems are computationally heavy and require a tremendous amount of data. A NN model is inextricably linked to the dataset on which it is trained. The cornerstone of this study is based on the data dependencieds of these models. The model herein is trained on two separate datasets and tested on three dataset in total in order to investigate the data dependency. When validating the model on their own datasets the model reached an accuracy of 92% respectively 99%. In comparison to previous work where an accuracy of 94% was reached. During evaluation of separate datasets, the model performed around the 60% range in 5 out of 6 cases, with the outlier of 29% in one of the cases.

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Section: Wildfire Segmentation Using Deep Vision Transformersmentioning

confidence: 72%

Using neural networks to detect fire from overhead images

Kurasinski

Tan

Malekian

2022

Preprint

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“…In the classification layer, the token sequence is passed as the input to a softmax function. Rafik et al [14] explored the potential of vision transformers in the context of forest fire segmentation using visible spectrum images. This framework improved upon two vision transformers, Medical Transformer [43] and TransUNet [44], for fire detection.…”

Section: Fire Detection and Segmentation Methods Based On Transformersmentioning

confidence: 99%

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

et al. 2021

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In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

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“…Ghali also explored the correlation of the popular allocation standard of subspace learning and designed the deep convolution and domain adaptive sample classification algorithm. The experimental effect was good ( Ghali et al, 2021 ).…”

Section: Introductionmentioning

confidence: 94%

“…The results reveal that the optimized structure can replace the traditional forest fire prediction model (Moayedi et al, 2020). Ghali et al (2021) used deep learning technology to establish a deep learning convolutional transfer learning feature extraction network. Ghali also explored the correlation of the popular allocation standard of subspace learning and designed the deep convolution and domain adaptive sample classification algorithm.…”

Section: Introductionmentioning

confidence: 99%

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

et al. 2022

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Forests are indispensable links in the ecological chain and important ecosystems in nature. The destruction of forests seriously influences the ecological environment of the Earth. Forest protection plays an important role in human sustainable development, and the most important aspect of forest protection is preventing forest fires. Fire affects the structure and dynamics of forests and also climate and geochemical cycles. Using various technologies to monitor the occurrence of forest fires, quickly finding the source of forest fires, and conducting early intervention are of great significance to reducing the damage caused by forest fires. An improved forest fire risk identification algorithm is established based on a deep learning algorithm to accurately identify forest fire risk in a complex natural environment. First, image enhancement and morphological preprocessing are performed on a forest fire risk image. Second, the suspected forest fire area is segmented. The color segmentation results are compared using the HAF and MCC methods, and the suspected forest fire area features are extracted. Finally, the forest fire risk image recognition processing is conducted. A forest fire risk dataset is constructed to compare different classification methods to predict the occurrence of forest fire risk to improve the backpropagation (BP) neural network forest fire identification algorithm. An improved machine learning algorithm is used to evaluate the classification accuracy. The results reveal that the algorithm changes the learning rate between 0.1 and 0.8, consistent with the cross-index verification of the 10x sampling algorithm. In the combined improved BP neural network and support vector machine (SVM) classifier, forest fire risk is recognized based on feature extraction and the BP network. In total, 1,450 images are used as the training set. The experimental results reveal that in image preprocessing, image enhancement technology using the frequency and spatial domain methods can enhance the useful information of the image and improve its clarity. In the image segmentation stage, MCC is used to evaluate the segmentationresults. The accuracy of this algorithm is high compared with other algorithms, up to 92.73%. Therefore, the improved forest fire risk identification algorithm can accurately identify forest fire risk in the natural environment and contribute to forest protection.

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Wildfire Segmentation Using Deep Vision Transformers

Cited by 65 publications

References 78 publications

Using neural networks to detect fire from overhead images

Using neural networks to detect fire from overhead images

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

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