Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark

Yar, Hikmat; Hussain, Tanveer; Agarwal, Mohit; Khan, Zulfiqar Ahmad; Gupta, Suneet Kumar; Baik, Sung Wook

doi:10.1109/tip.2022.3207006

Cited by 58 publications

(23 citation statements)

References 59 publications

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“…Both indoor and outdoor surveillance results confirmed the effectiveness of the proposed framework. In comparison to recent techniques, this reserch work has achieved an increase of 0.8% accuracy with the proposed framework against [ 30 , 40 , 48 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. Compared to the existing frameworks on violent flow, the proposed framework shows a 0.8% increase in accuracy compared to [ 8 , 57 , 67 , 68 ].…”

Section: Resultsmentioning

confidence: 88%

“…The literature describes a variety of CNN-based models for fire detection [ 39 , 40 ], medical images [ 41 ], classifying videos [ 8 , 42 ], predicting time series data [ 43 , 44 , 45 ], forecasting [ 46 ], etc. Several CNN architectures have been used for feature extraction in recent literature, including EfficientNet [ 47 ], Squeeze Net, Google Net, and MobileNet, among others.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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An IoT Enable Anomaly Detection System for Smart City Surveillance

Islam¹,

Dukyil²,

Alyahya³

et al. 2023

Sensors

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Since the advent of visual sensors, smart cities have generated massive surveillance video data, which can be intelligently inspected to detect anomalies. Computer vision-based automated anomaly detection techniques replace human intervention to secure video surveillance applications in place from traditional video surveillance systems that rely on human involvement for anomaly detection, which is tedious and inaccurate. Due to the diverse nature of anomalous events and their complexity, it is however, very challenging to detect them automatically in a real-world scenario. By using Artificial Intelligence of Things (AIoT), this research work presents an efficient and robust framework for detecting anomalies in surveillance large video data. A hybrid model integrating 2D-CNN and ESN are proposed in this research study for smart surveillance, which is an important application of AIoT. The CNN is used as feature extractor from input videos which are then inputted to autoencoder for feature refinement followed by ESN for sequence learning and anomalous events detection. The proposed model is lightweight and implemented over edge devices to ensure their capability and applicability over AIoT environments in a smart city. The proposed model significantly enhanced performance using challenging surveillance datasets compared to other methods.

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Section: Resultsmentioning

confidence: 88%

Section: The Proposed Methodsmentioning

confidence: 99%

An IoT Enable Anomaly Detection System for Smart City Surveillance

Islam¹,

Dukyil²,

Alyahya³

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…These AI-based methods are constructed via shallow architecture, requiring handcrafted feature engineering and having limited generalization capabilities [ 31 ]. Furthermore, in AI-based methods, CNN and RNNs achieved better performance; however, using CNN, the feature is extracted in spatial dimensions [ 32 , 33 , 34 ], while the RNNS learns in temporal dimensions, while solar power generation includes both types of features. Therefore, an approach with the ability of spatial and temporal feature extraction is required for accurate solar power prediction.…”

Section: Introductionmentioning

confidence: 99%

Solar Power Prediction Using Dual Stream CNN-LSTM Architecture

Alharkan

Habib

Islam³

2023

Sensors

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The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power is challenging due to the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual-stream convolutional neural network (CNN) and long short-term nemory (LSTM) network followed by a self-attention mechanism network (DSCLANet). Here, CNN is used to learn spatial patterns and LSTM is incorporated for temporal feature extraction. The output spatial and temporal feature vectors are then fused, followed by a self-attention mechanism to select optimal features for further processing. Finally, fully connected layers are incorporated for short-term solar power prediction. The performance of DSCLANet is evaluated on DKASC Alice Spring solar datasets, and it reduces the error rate up to 0.0136 MSE, 0.0304 MAE, and 0.0458 RMSE compared to recent state-of-the-art methods.

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“…Authors have demonstrated that on LIDC-IDRI lung dataset, proposed CNN can be reduced by 90.3% in size while maintaining the performance. Yar et al [ 53 ] have also used Differential Evolution for compression of attention based InceptionV3 for Fire images classification.…”

Section: Introductionmentioning

confidence: 99%

Development of a compressed FCN architecture for semantic segmentation using Particle Swarm Optimization

Agarwal

Gupta

Biswas

2023

Neural Comput & Applic

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Researchers have adapted the conventional deep learning classification networks to generate Fully Conventional Networks (FCN) for carrying out accurate semantic segmentation. However, such models are expensive both in terms of storage and inference time and not readily employable on edge devices. In this paper, a compressed version of VGG16-based Fully Convolution Network (FCN) has been developed using Particle Swarm Optimization. It has been shown that the developed model can offer tremendous saving in storage space and also faster inference time, and can be implemented on edge devices. The efficacy of the proposed approach has been tested using potato late blight leaf images from publicly available PlantVillage dataset, street scene image dataset and lungs X-Ray dataset and it has been shown that it approaches the accuracies offered by standard FCN even after 851× compression.

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Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark

Cited by 58 publications

References 59 publications

An IoT Enable Anomaly Detection System for Smart City Surveillance

An IoT Enable Anomaly Detection System for Smart City Surveillance

Solar Power Prediction Using Dual Stream CNN-LSTM Architecture

Development of a compressed FCN architecture for semantic segmentation using Particle Swarm Optimization

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