Abstract
Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real-time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challenges, early fire detection and automatic response are very significant. Therefore, we develop a novel framework based on a lightweight convolutional neural network (CNN), requiring less training time, and it is applicable over resource-constrained devices. The internal architecture of the proposed model is inspired by the block-wise VGG16 architecture with a significantly reduced number of parameters, input size, inference time, and comparatively higher accuracy for early fire detection. In the proposed model, small-size uniform convolutional filters are employed that are specifically designed to capture fine details of input fire images with a sequentially increasing number of channels to aid effective feature extraction. The proposed model is evaluated on two datasets such as a benchmark Foggia's dataset and our newly created small-scaled fire detection dataset with extremely challenging real-world images containing a high-level of diversity. Experimental results conducted on both datasets reveal the better performance of the proposed model compared to state-of-the-art in terms of accuracy, false-positive rate, model size, and running time, which indicates its robustness and feasible installation in real-world scenarios.
Highlights
Wildfire, an extremely catastrophic disaster, leads to the destruction of forests, human assets, yielding reduced soil fertility, and land resources and is a major cause of global warming
DL provides end-to-end feature extraction mechanism, but it requires a large amount of training data and is computationally expensive. erefore, in this paper, we developed a lightweight (LW-convolutional neural network (CNN)) model with better detection accuracy, low false alarm rates, and the potential to be deployed over resource-constrained devices (RCD). e major contributions of this research work are summarized as follows: (i) Tackling the limited computational resources challenge of real-world IoT devices, we introduce a lightweight deep model, functional over RCD in real-time. e proposed model achieves better accuracy with a limited number of learning parameters, i.e., 2.01 and 0.94 million reduced parameters when compared to famous lightweight NASNetMobile and MobileNetV1 networks
All the models including ours are trained using 30 epochs with a small learning rate so that most of the previously acquired knowledge can be retained in the network. e pretrained model moderately updates the learning parameters for achieving optimal results on the target dataset. e various hyperparameters used in our ablation experiments are presented in Table 2. us, we used the default input size of each network to retrain with a batch size of 32, and the stochastic gradient descent optimizer with momentum (SGD-M) set to 1e4 with 0.9 momentum
Summary
An extremely catastrophic disaster, leads to the destruction of forests, human assets, yielding reduced soil fertility, and land resources and is a major cause of global warming. Roughout the globe, wildfires, building fires, and vehicle fires have a huge impact on global warming, the ecosystem, and the economy, resulting loss of living beings. According to e National Fire Data System (NFDS), in South Korea, a total of 24,539 structure fire cases were reported, causing 250 deaths, 1,646 injuries, and direct property damage of 705,960 USD from September 2020 to September 2021 [2]. From September 2020 to September 2021, in South Korea, 78,219 vehicle fires occurred, which caused 461 deaths, 1,875 injuries, and property damage of 357,609 USD [3]
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