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

Wang, Zewei; Zheng, Change; Yin, Jun; Tian, Yongchao; Cui, Wei

doi:10.3390/electronics10212675

Cited by 6 publications

(3 citation statements)

References 50 publications

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“…In smoke detection, it becomes particularly hard given that the smoke target is not well defined, as diffusion introduces ambiguity in the precise location of smoke. In [19] a new method is proposed to solve this problem, utilizing concentration weight labeling by incorporating a mask over the ground truth label based on the relationship to pixel values. The authors applied an encoder-decoder architecture with MobileNet as the downsampling layer, and PSPnet as the upsampling layer, with a weighted loss function and 4 smoke categories -Thick smoke, Thin smoke, Thick smoke and clouds, and Thin smoke and clouds.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Wildfire Detection With Deep Learning—A Case Study for the CICLOPE Project

Gonçalves,

Brandão,

Ferreira

2024

IEEE Access

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In recent years, Portugal has seen wide variability in wildfire damage associated to high unpredictability of climatic events such as severe heatwaves and drier summers. Therefore, timely and accurate detection of forest and rural fires is of great importance for successful fire containment and suppression efforts, as wildfires exponentially increase the spread rate from the moment of ignition. In the field of early smoke detection, the CICLOPE project currently trailblazes in the employment of a network of Remote Acquisition Towers for wildfire prevention and observation, along with a rule-based automatic smoke detection system, covering over 2, 700, 000 hectares of wildland and rural area in continental Portugal. However, the inherent challenges of automatic smoke detection raise issues of high false alarm rates that affect the system's prediction quality and overwhelm the Management and Control Centers with numerous false alarms. The research work presented in this paper evaluates the potential improvement in wildfire smoke detection accuracy and specificity using deep learning-based architectures. It proposes a solution based on a Dual-Channel CNN that can be deployed as a secondary prediction confirmation layer to further refine the CICLOPE automatic smoke detection system. The proposed solution takes advantage of the high true alarm coverage of the current detection system by taking only the predicted alarm images and respective bounding box coordinates as inputs. The Dual-Channel network combines state-of-the-art DenseNet architecture with a novel detail selective network with spatial and channel attention modules trained separately with image data obtained from CICLOPE, fusing the extracted features from both networks in a concatenation layer. The results demonstrate that the proposed Dual-Channel CNN outperforms both single-channel networks achieving an accuracy of 99.7% and a low false alarm rate of 0.20% when re-examining the alarms produced by the CICLOPE surveillance system.

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Section: Semantic Segmentationmentioning

confidence: 99%

Wildfire Detection With Deep Learning—A Case Study for the CICLOPE Project

Gonçalves,

Brandão,

Ferreira

2024

IEEE Access

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“…Researchers have proposed various methods for smoke segmentation, including a concentration weightingbased approach to address the challenges posed by the transparency, fuzzy contour, and concentration diversity of smoke. Wang et al [40] proposed a forest fire smoke semantic segmentation method based on concentration weighting to address the problem of smoke label uncertainty leading to the degradation of smoke segmentation capability caused by the transparency, fuzzy contour, and concentration diversity possessed by smoke in the supervised smoke segmentation task. The method built the mathematical relationship between smoke concentrations and its pixel values utilizing the clue that different concentrations of smoke reflect different pixel values in an image.…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Smoke Detection Based on Multiple Color Spaces Deep Feature Fusion

Han,

Tian,

Zheng

et al. 2024

Forests

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The drastic increase of forest fire occurrence, which in recent years has posed severe threat and damage worldwide to the natural environment and human society, necessitates smoke detection of the early forest fire. First, a semantic segmentation method based on multiple color spaces feature fusion is put forward for forest fire smoke detection. Considering that smoke images in different color spaces may contain varied and distinctive smoke features which are beneficial for improving the detection ability of a model, the proposed model integrates the function of multi-scale and multi-type self-adaptive weighted feature fusion with attention augmentation to extract the enriched and complementary fused features of smoke, utilizing smoke images from multi-color spaces as inputs. Second, the model is trained and evaluated on part of the FIgLib dataset containing high-quality smoke images from watchtowers in the forests, incorporating various smoke types and complex background conditions, with a satisfactory smoke segmentation result for forest fire detection. Finally, the optimal color space combination and the fusion strategy for the model is determined through elaborate and extensive experiments with a superior segmentation result of 86.14 IoU of smoke obtained.

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“…There are two types of these algorithms: traditional and new generation. The method includes a new fire extension model, a distributed algorithm for unmanned aerial vehicles to relocate to the expanding front line of fires [47], an on-site detection system for real-time detection of fire pixels using Deeplabv3+ [48], CNN-based smoke detection and segmentation suitable for sunny and hazy environments [49], a U-Net model with ResNet50 as the backbone network [50], a DeepLabV3+ model with ResNet50 [50], and a semantic segmentation method based on concentration weighting [51].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Fire Detection Using Dynamic Threshold Grayscale Segmentation and Residual Network Transfer Learning

Li,

Sun

2023

Mathematics

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Research on fire detection based on deep learning has been increasing lately, and current models differ in accuracy and computational complexity. To reduce the loss of semantic information and improve prediction accuracy, this paper combines dynamic threshold grayscale segmentation and residual network transfer learning. An effective training data extension strategy integrates the experimental data of a real fire platform and a forest-image-based fire database, where the experimental data of the real fire platform are shot based on different lighting and backgrounds. It has been proven that this is a good solution that can effectively solve the problem of training robust deep models relying on datasets with large diversity, which can improve the overall performance of the training model. Then, we introduce a network architecture based on dynamic threshold grayscale segmentation and residual net transfer learning, which reaches residual net level accuracy on image-based fire detection tasks with less computation. We train the model with a self-built image dataset, test it on a general fire image dataset, and evaluate it on a self-built fire video dataset. In numerous experiments, our method produced a training and testing accuracy of 98.26% and 99.90%, respectively, with corresponding losses of 0.036 and 0.04. The evaluation accuracy was 90% under the condition of a complex sample environment. Compared with other models, our method has advantages in accuracy, training loss, and cost time. Comprehensive comparisons show effectiveness and improved overall performance.

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

Cited by 6 publications

References 50 publications

Wildfire Detection With Deep Learning—A Case Study for the CICLOPE Project

Wildfire Detection With Deep Learning—A Case Study for the CICLOPE Project

Forest Fire Smoke Detection Based on Multiple Color Spaces Deep Feature Fusion

Image-Based Fire Detection Using Dynamic Threshold Grayscale Segmentation and Residual Network Transfer Learning

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