Forest Fire Detection System Using IoT and Artificial Neural Network

Dubey, Vinay; Kumar, Prashant; Chauhan, Naveen

doi:10.1007/978-981-13-2324-9_33

Cited by 59 publications

(18 citation statements)

References 15 publications

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“…In contrast to building and vehicle fires, wildfires are the most dangerous disasters that affect the life cycle of nature. There are many causes of wildfires such as rising temperatures, changing climate, lightning from clouds, sparking from falling rocks, or rubbing dry trees during summers [ 4 ]. The devastation caused by wildfires has risen over the past two decades in the United States and other countries around the globe.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Yar

Hussain

Khan

et al. 2021

Computational Intelligence and Neuroscience

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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.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Yar

Hussain

Khan

et al. 2021

Computational Intelligence and Neuroscience

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“…For outdoor applications, vision-based fire detection systems have a higher potential to achieve better performance than sensor-based systems [44]. Supporting this idea, for the detection of forest fires, the accuracy has been reported to be above 95% for vision-based detectors [11], [13], [14] while it is between 80% and 95% for sensorbased detectors [21], [25], [26]. One of the main reasons is that the concentration levels of gases may be too low to detect fire via sensor-based systems.…”

Section: Related Workmentioning

confidence: 95%

“…Each of References [21]- [24] uses the sensor network that is generally comprised of multiple fire detectors located separately. The sensor networks are also used for the outdoor fire detection with the Artificial Intelligence (AI)-based decision systems, which are specifically designed by using NNs in [25], [26], and Deep Learning model in [27]. Moreover, References [28]- [30] combine the sensor networks with vision-based systems in order to detect the outdoor fire accurately.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection

Nakıp

Güzeliş

Yıldız³

2021

IEEE Access

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We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall-τ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications.

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“…For instance, Sun et al [19] developed a CNN for forest fire recognition, augmented the limited training samples by randomly initializing parameters, and achieved a good effect in fire classification. Heyns et al [20] integrated traditional recognition method with neural network: AdaBoost and local binary pattern (LBP) were employed to initially recognize the images and extract the candidate regions for flames; Then, the features were extracted from the candidate regions and classified by the CNN. Attri et al [21] applied the deep belief network (DBN) to recognize flames.…”

Section: Literature Reviewmentioning

confidence: 99%

Forest Fire Recognition Based on Feature Extraction from Multi-View Images

Wu¹,

Zhang²,

Ji³

et al. 2021

115

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Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.

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Forest Fire Detection System Using IoT and Artificial Neural Network

Cited by 59 publications

References 15 publications

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments

Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection

Forest Fire Recognition Based on Feature Extraction from Multi-View Images

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