Convolutional neural network for smoke and fire semantic segmentation

Frizzi, Sébastien; Bouchouicha, Moez; Ginoux, Jean-Marc; Moreau, Éric; Sayadi, Mounir

doi:10.1049/ipr2.12046

Cited by 61 publications

(32 citation statements)

References 34 publications

(48 reference statements)

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“…This method showed good results (accuracy of 93.4% and segmentation time per image of 21.1 s) using data augmentation techniques such as rotation, flip, changing brightness/contrast, crop, and adding noises. It outperformed previous published models and proved its efficiency in detecting and classifying fire/smoke pixels [61].…”

Section: Fire Segmentation Using Deep Learning Approaches For Uavmentioning

confidence: 77%

“…Using a dropout strategy and the FLAME dataset, U-Net obtained an F1-score of 87.75% and proved its ability to segment wildfire and identify the precise shapes of flames [28]. Frizzi et al [61] also proposed a method based on VGG16 to segment both smoke and fire. This method showed good results (accuracy of 93.4% and segmentation time per image of 21.1 s) using data augmentation techniques such as rotation, flip, changing brightness/contrast, crop, and adding noises.…”

Section: Fire Segmentation Using Deep Learning Approaches For Uavmentioning

confidence: 99%

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Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

Ghali

Akhloufi

Mseddi

2022

Sensors

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Wildfires are a worldwide natural disaster causing important economic damages and loss of lives. Experts predict that wildfires will increase in the coming years mainly due to climate change. Early detection and prediction of fire spread can help reduce affected areas and improve firefighting. Numerous systems were developed to detect fire. Recently, Unmanned Aerial Vehicles were employed to tackle this problem due to their high flexibility, their low-cost, and their ability to cover wide areas during the day or night. However, they are still limited by challenging problems such as small fire size, background complexity, and image degradation. To deal with the aforementioned limitations, we adapted and optimized Deep Learning methods to detect wildfire at an early stage. A novel deep ensemble learning method, which combines EfficientNet-B5 and DenseNet-201 models, is proposed to identify and classify wildfire using aerial images. In addition, two vision transformers (TransUNet and TransFire) and a deep convolutional model (EfficientSeg) were employed to segment wildfire regions and determine the precise fire regions. The obtained results are promising and show the efficiency of using Deep Learning and vision transformers for wildfire classification and segmentation. The proposed model for wildfire classification obtained an accuracy of 85.12% and outperformed many state-of-the-art works. It proved its ability in classifying wildfire even small fire areas. The best semantic segmentation models achieved an F1-score of 99.9% for TransUNet architecture and 99.82% for TransFire architecture superior to recent published models. More specifically, we demonstrated the ability of these models to extract the finer details of wildfire using aerial images. They can further overcome current model limitations, such as background complexity and small wildfire areas.

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Section: Fire Segmentation Using Deep Learning Approaches For Uavmentioning

confidence: 77%

Section: Fire Segmentation Using Deep Learning Approaches For Uavmentioning

confidence: 99%

Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

Ghali

Akhloufi

Mseddi

2022

Sensors

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“…In order to evaluate the effectiveness of the weighted method, comparative experiments with and without the weight were conducted on several common semantic segmentation networks, such as FCN [56], Segnet [57] and Unet [58], and a forest fire smoke detection method, Frizzi [39]. The control coefficient and weighted loss function type for all the tested network architectures have been determined by alike experiments conducted in 3.4, as shown in Table 5.…”

Section: Discussionmentioning

confidence: 99%

“…Semantic segmentation based on CNN, with the input of an arbitrary-size image, utilizes a set of convolutional layers, non-linear activation functions, pooling and upsampling layers to output a predicted image [34][35][36][37][38]. Moreover, CNNs have achieved a lot of significant results in the field of vision detection of forest fire smoke [39,40].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

Wang

Zheng

Yin³

et al. 2021

Electronics

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Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.

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“…Based on the spatial features, Kim et al [29] applied Faster R-CNN to detect suspected fire regions, which LSTM then used to interpret the dynamic fire behavior. Apart from detection, some CNNs also allowed for segmenting the fire in an image [30], [31]. One limitation of CNN-based approaches is that they suffer if fire regions are small, which is an inherent limitation of conventional CNNs due to their fixed-size receptive fields [32].…”

Section: A Fire Detectionmentioning

confidence: 99%

Spatio-Temporal Self-Attention Network for Fire Detection and Segmentation in Video Surveillance

Shahid

Virtusio

et al. 2022

IEEE Access

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Convolutional Neural Network (CNN) based approaches are popular for various image/video related tasks due to their state-of-the-art performance. However, for problems like object detection and segmentation, CNNs still suffer from objects with arbitrary shapes or sizes, occlusions, and varying viewpoints. This problem makes it mostly unsuitable for fire detection and segmentation since flames can have an unpredictable scale and shape. In this paper, we propose a method that detects and segments fireregions with special considerations of their arbitrary sizes and shapes. Specifically, our approach uses a self-attention mechanism to augment spatial characteristics with temporal features, allowing the network to reduce its reliance on spatial factors like shape or size and take advantage of robust spatial-temporal dependencies. As a whole, our pipeline has two stages: In the first stage, we take out region proposals using Spatial-Temporal features, and in the second stage, we classify whether each region proposal is flame or not. Due to the scarcity of generous fire datasets, we adopt a transfer learning strategy to pre-train our classifier with the ImageNet dataset. Additionally, our Spatial-Temporal Network only requires semi-supervision, where it only needs one ground-truth segmentation mask per frame-sequence input. The experimental results of our proposed method significantly outperform the state-of-the-art fire detection with a 2 ∼ 4% relative enhancement in F1-score for large scale fires and a nearly ∼ 60% relative improvement for small fires at a very early stage.

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Convolutional neural network for smoke and fire semantic segmentation

Cited by 61 publications

References 34 publications

Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

Spatio-Temporal Self-Attention Network for Fire Detection and Segmentation in Video Surveillance

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