A Lightweight Convolution Neural Network for Automatic Disasters Recognition

Munsif, Muhammad; Afridi, Hina; Ullah, Mohib; Khan, Sultan Daud; Cheikh, Faouzi Alaya; Sajjad, Muhammad

doi:10.1109/euvip53989.2022.9922799

Cited by 11 publications

(2 citation statements)

References 24 publications

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“…MobileNetV1 and NASNetMobile are specifically engineered to ensure prompt and predictable response times, making them wellsuited for applications requiring rapid processing [52]. These architectures offer significant advantages in terms of computational efficiency, making them favorable choices in various scientific and professional contexts [53]. Taking into account real-world implementation, resource computing cost, and suppression of restrictions in existing lightweight models, this paper offers FlameNet, an effective lightweight fire classification and detection model.…”

Section: Deep Features Extractionmentioning

confidence: 99%

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Nadeem,

Dilshad,

Alghamdi

et al. 2023

Smart Cities

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The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.

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Section: Deep Features Extractionmentioning

confidence: 99%

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Nadeem,

Dilshad,

Alghamdi

et al. 2023

Smart Cities

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“…Rescuers can utilize UAVs to locate victims who have fallen into the sea, and tracking mostly depended on the tracking algorithms [4]. Visual object tracking has historically been one of the primary applications of computer vision and has been extensively studied [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Siamese Object Tracking Algorithm Based on Knowledge Distillation in Marine Environment

et al. 2023

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Siamese networks have gained considerable attention for object tracking due to their balance of speed and accuracy. However, existing Siamese tracking algorithms have been too rigid in their predictions of bounding box tags and lack uncertainty estimation, resulting in poor tracking performance in marine environments, particularly those with waves. To improve the effectiveness of trackers in marine environments, this study proposes a Siamese distillation network. First, to address the issue that the presence of waves and other disturbances may result in target loss or inaccuracy when tracking the target, the concept of a probability distribution of the bounding box is introduced in this study, which transforms the standard Dirac delta distribution of the bounding box into a probability distribution of the bounding box, effectively reducing the impact of interference on tracking performance and improving target location accuracy. Second, we chose ResNet100 as the backbone network to obtain richer features for localization. Finally, this work offers a knowledge distillation approach to further enhance the tracking accuracy and model performance, while considering the impact of the model's number of parameters and computational amount on tracking performance. This network outperforms most trackers in terms of accuracy, according to extensive experimental results, and performs well on the target tracking benchmark and marine dataset annotated in this study. Specifically, this network achieved the highest accuracy value of 0.612 compared to other Siamese networks, resulting in a 2.5% increase compared to original baseline network. This suggests that the proposed algorithm is practical.INDEX TERMS Bounding box probability distribution, knowledge distillation, object tracking, siamese network.

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A novel approach based on convolutional neural networks ensemble for fire detection

Belarbi,

Hassini,

Benamara

2024

SIViP

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A Lightweight Convolution Neural Network for Automatic Disasters Recognition

Cited by 11 publications

References 24 publications

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Research on Siamese Object Tracking Algorithm Based on Knowledge Distillation in Marine Environment

A novel approach based on convolutional neural networks ensemble for fire detection

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