FBC-ANet: A Semantic Segmentation Model for UAV Forest Fire Images Combining Boundary Enhancement and Context Awareness

Zhang, Lin; Wang, Mingyang; Ding, Yunhong; Wan, Tingting; Qi, Bo; Pang, Yutian

doi:10.3390/drones7070456

Cited by 6 publications

(1 citation statement)

References 46 publications

(58 reference statements)

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“…This method holds promise for enhancing forest fire monitoring and response efforts. Lin Zhang et al [23] proposed the FBC-ANet network, combining boundary enhancement and context-aware modules in a lightweight structure. This innovative approach achieved impressive results, with a segmentation accuracy of 92.19%, F1 score of 90.76%, and IoU of 83.08% on UAV images from the FLAME dataset.…”

Section: Related Work 21 Forest Fire Segmentationmentioning

confidence: 99%

FlameTransNet: Advancing Forest Flame Segmentation with Fusion and Augmentation Techniques

Chen,

Bai,

Lin

et al. 2023

Forests

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Forest fires pose severe risks, including habitat loss and air pollution. Accurate forest flame segmentation is vital for effective fire management and protection of ecosystems. It improves detection, response, and understanding of fire behavior. Due to the easy accessibility and rich information content of forest remote sensing images, remote sensing techniques are frequently applied in forest flame segmentation. With the advancement of deep learning, convolutional neural network (CNN) techniques have been widely adopted for forest flame segmentation and have achieved remarkable results. However, forest remote sensing images often have high resolutions, and relative to the entire image, forest flame regions are relatively small, resulting in class imbalance issues. Additionally, mainstream semantic segmentation methods are limited by the receptive field of CNNs, making it challenging to effectively extract global features from the images and leading to poor segmentation performance when relying solely on labeled datasets. To address these issues, we propose a method based on the deeplabV3+ model, incorporating the following design strategies: (1) an adaptive Copy-Paste data augmentation method is introduced to learn from challenging samples (Images that cannot be adequately learned due to class imbalance and other factors) effectively, (2) transformer modules are concatenated and parallelly integrated into the encoder, while a CBAM attention mechanism is added to the decoder to fully extract image features, and (3) a dice loss is introduced to mitigate the class imbalance problem. By conducting validation on our self-constructed dataset, our approach has demonstrated superior performance across multiple metrics compared to current state-of-the-art semantic segmentation methods. Specifically, in terms of IoU (Intersection over Union), Precision, and Recall metrics for the flame category, our method has exhibited notable enhancements of 4.09%, 3.48%, and 1.49%, respectively, when compared to the best-performing UNet model. Moreover, our approach has achieved advancements of 11.03%, 9.10%, and 4.77% in the same aforementioned metrics as compared to the baseline model.

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Section: Related Work 21 Forest Fire Segmentationmentioning

confidence: 99%

FlameTransNet: Advancing Forest Flame Segmentation with Fusion and Augmentation Techniques

Chen,

Bai,

Lin

et al. 2023

Forests

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Firefighting Robot Extinguishment Decision‐Making Based on Visual Guidance: A Novel Attention and Scale U‐Net Model and Genetic Algorithm

Zhao,

Li,

Zhu

et al. 2024

Journal of Field Robotics

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At present, rescue firefighting relies mainly on firefighting robots, and robots with perception and decision‐making functions are the key elements for achieving intelligent firefighting. However, traditional firefighting robots often lack the ability for autonomous perception and decision‐making when extinguishing multiple fire sources, leading to low rescue efficiency and increased risk for rescue personnel, especially when making firefighting decisions in extreme fire scenes, which poses a challenge. To effectively handle firefighting tasks and ensure operational efficiency, a robot firefighting decision‐making method based on drone visual guidance is proposed. First, we introduce a novel Attention and Scale U‐Net (ASUNet) model to accurately capture crucial target information, including fire location and size, in a fire scene. The ASUNet model adopts an effective multiscale fusion strategy and attention mechanism to enhance the model's performance. Subsequently, based on the results of the ASUNet model, through pixel segmentation clustering and a genetic optimization algorithm, we obtain the robot's firefighting decision results, thereby guiding the robot to carry out firefighting operations systematically. Finally, through numerical experiments, it is verified that the proposed ASUNet model is superior and effective, as the model can perceive important information in a fire scene and extract it well. The use of improved genetic optimization can further accelerate algorithm convergence. To our knowledge, this study is the first to use drone‐based monocular vision guidance for firefighting decision‐making, providing significant engineering value.

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Wildfire Impact Analysis and Spread Dynamics Estimation on Satellite Images Using Deep Learning

Priya,

Vani

2024

J Indian Soc Remote Sens

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FBC-ANet: A Semantic Segmentation Model for UAV Forest Fire Images Combining Boundary Enhancement and Context Awareness

Cited by 6 publications

References 46 publications

FlameTransNet: Advancing Forest Flame Segmentation with Fusion and Augmentation Techniques

FlameTransNet: Advancing Forest Flame Segmentation with Fusion and Augmentation Techniques

Firefighting Robot Extinguishment Decision‐Making Based on Visual Guidance: A Novel Attention and Scale U‐Net Model and Genetic Algorithm

Wildfire Impact Analysis and Spread Dynamics Estimation on Satellite Images Using Deep Learning

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