Advancing fire detection: two-stage deep learning with hybrid feature extraction using faster R-CNN approach

Cheknane, Maroua; Bendouma, Tahar; Boudouh, Saida Sarra

doi:10.1007/s11760-024-03250-w

SIViP

2024

DOI: 10.1007/s11760-024-03250-w

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Advancing fire detection: two-stage deep learning with hybrid feature extraction using faster R-CNN approach

Maroua Cheknane,

Tahar Bendouma,

Saida Sarra Boudouh

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Cited by 3 publications

References 14 publications

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A Multi-Scale Approach to Early Fire Detection in Smart Homes

Abdusalomov,

Umirzakova,

Safarov

et al. 2024

Electronics

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In recent years, advancements in smart home technologies have underscored the need for the development of early fire and smoke detection systems to enhance safety and security. Traditional fire detection methods relying on thermal or smoke sensors exhibit limitations in terms of response time and environmental adaptability. To address these issues, this paper introduces the multi-scale information transformer–DETR (MITI-DETR) model, which incorporates multi-scale feature extraction and transformer-based attention mechanisms, tailored specifically for fire detection in smart homes. MITI-DETR achieves a precision of 99.00%, a recall of 99.50%, and a mean average precision (mAP) of 99.00% on a custom dataset designed to reflect diverse lighting and spatial conditions in smart homes. Extensive experiments demonstrate that MITI-DETR outperforms state-of-the-art models in terms of these metrics, especially under challenging environmental conditions. This work provides a robust solution for early fire detection in smart homes, combining high accuracy with real-time deployment feasibility.

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A Multi-Scale Approach to Early Fire Detection in Smart Homes

Abdusalomov,

Umirzakova,

Safarov

et al. 2024

Electronics

View full text Add to dashboard Cite

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Advanced Object Detection for Maritime Fire Safety

Makhmudov,

Umirzakova,

Kutlimuratov

et al. 2024

Fire

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In this study, we propose an advanced object detection model for fire and smoke detection in maritime environments, leveraging the DETR (Detection with Transformers) framework. To address the specific challenges of shipboard fire and smoke detection, such as varying lighting conditions, occlusions, and the complex structure of ships, we enhance the baseline DETR model by integrating EfficientNet-B0 as the backbone. This modification aims to improve detection accuracy while maintaining computational efficiency. We utilize a custom dataset of fire and smoke images captured from diverse shipboard environments, incorporating a range of data augmentation techniques to increase model robustness. The proposed model is evaluated against the baseline DETR and YOLOv5 variants, showing significant improvements in Average Precision (AP), especially in detecting small and medium-sized objects. Our model achieves a superior AP score of 38.7 and outperforms alternative models across multiple IoU thresholds (AP50, AP75), particularly in scenarios requiring high precision for small and occluded objects. The experimental results highlight the model’s efficacy in early fire and smoke detection, demonstrating its potential for deployment in real-time maritime safety monitoring systems. These findings provide a foundation for future research aimed at enhancing object detection in challenging maritime environments.

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An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection

Altaf,

Yasir,

Dilshad

et al. 2025

Fire

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Globally, fire incidents cause significant social, economic, and environmental destruction, making early detection and rapid response essential for minimizing such devastation. While various traditional machine learning and deep learning techniques have been proposed, their detection performances remain poor, particularly due to low-resolution data and ineffective feature selection methods. Therefore, this study develops a novel framework for accurate fire detection, especially in challenging environments, focusing on two distinct phases: preprocessing and model initializing. In the preprocessing phase, super-resolution is applied to input data using LapSRN to effectively enhance the data quality, aiming to achieve optimal performance. In the subsequent phase, the proposed network utilizes an attention-based deep neural network (DNN) named Xception for detailed feature selection while reducing the computational cost, followed by adaptive spatial attention (ASA) to further enhance the model’s focus on a relevant spatial feature in the training data. Additionally, we contribute a medium-scale custom fire dataset, comprising high-resolution, imbalanced, and visually similar fire/non-fire images. Moreover, this study conducts an extensive experiment by exploring various pretrained DNN networks with attention modules and compares the proposed network with several state-of-the-art techniques using both a custom dataset and a standard benchmark. The experimental results demonstrate that our network achieved optimal performance in terms of precision, recall, F1-score, and accuracy among different competitive techniques, proving its suitability for real-time deployment compared to edge devices.

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Advancing fire detection: two-stage deep learning with hybrid feature extraction using faster R-CNN approach

Cited by 3 publications

References 14 publications

A Multi-Scale Approach to Early Fire Detection in Smart Homes

A Multi-Scale Approach to Early Fire Detection in Smart Homes

Advanced Object Detection for Maritime Fire Safety

An Optimized Deep-Learning-Based Network with an Attention Module for Efficient Fire Detection

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