ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition

Zhang, Jianmei; Zhu, Hongqing; Wang, Pengyu; Ling, Xiaofeng

doi:10.1109/access.2021.3050628

Cited by 81 publications

(33 citation statements)

References 40 publications

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“…Owing to the successful performance of various tasks, such as image segmentation, numerous studies have been conducted based on the encoder-decoder structure to segment objects. For example, Zhang et al [34] presented an efficient DL model based on U-Net and SqueezeNet for forest fire detection and recognition. The proposed framework is divided into two stages: a segmentation module that extracts the shape of a fire and a classification module that determines whether the detected fire area is correct.…”

Section: Fire Detection and Segmentation Methods Based On DLmentioning

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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Section: Fire Detection and Segmentation Methods Based On DLmentioning

confidence: 99%

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

et al. 2021

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“…Due to the fact that the channel features in high dimension space are redundant, channel compression is an important way to reduce the number of network parameters. Inspired by [41], Figure 2 shows the direct connection of convolution layers and the designed convolution 'Fire' module. The mathematical formulas on the blue box and orange box represent the feature of corresponding size and convolution layers with specific kernel size, respectively.…”

Section: Network Structure Of Sr-cnnmentioning

confidence: 99%

Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

et al. 2021

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High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.

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“…Therefore, we adopt two-stage models in which regional proposal and classification are performed sequentially in two steps. Two-stage models, such as ATT Squeeze U-Net [11], which was recently developed, obtained an accuracy of 0.93 and detection frame rate of 1.1 fps (0.89 s per image). In addition, the Faster R-CNN was used with three different base networks, AlexNet, VGG16, and ResNet101 [8], for forestfire detection.…”

Section: Introductionmentioning

confidence: 99%

Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data

et al. 2022

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An effective forest-fire response is critical for minimizing the losses caused by forest fires. The purpose of this study is to construct a model for early fire detection and damage area estimation for response systems based on deep learning. First, a large-scale fire dataset with approximately 400,000 images is used to train and test object-detection models. The optimal backbone for the faster region-based convolutional neural network (Faster R-CNN) model is determined using a DetNAS-based architecture search algorithm. Then, the searched light-weight backbone is compared with well-known backbones, such as ResNet, VoVNet, and FBNetV3. In addition, data pertaining to six years of historical forest fire events are employed to estimate the damaged area. Subsequently, a weather API is used to match the recorded events. A Bayesian neural network (BNN) model is used as a regression model to estimate the damaged area. Additionally, the trained model is compared with other widely used regression models, such as decision trees and neural networks. The Faster R-CNN with a searched backbone achieves a mean average precision of 27.9 on 40,000 testing images, outperforming existing backbones. Compared with other regression models, the BNN estimates the damage area with less error and increased generalization. Thus, both proposed models demonstrate their robustness and suitability for implementation in real-world systems. INDEX TERMSForest-fire management, Deep learning, Bayesian neural network, Object detection.

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ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition

Cited by 81 publications

References 40 publications

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

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

Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data

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