Drone-Based Wildfire Detection with Multi-Sensor Integration DOI Creative Commons
Akmalbek Abdusalomov, Sabina Umirzakova,

Makhkamov Bakhtiyor Shukhratovich

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4651 - 4651

Published: Dec. 12, 2024

Wildfires pose a severe threat to ecological systems, human life, and infrastructure, making early detection critical for timely intervention. Traditional fire systems rely heavily on single-sensor approaches are often hindered by environmental conditions such as smoke, fog, or nighttime scenarios. This paper proposes Adaptive Multi-Sensor Oriented Object Detection with Space–Frequency Selective Convolution (AMSO-SFS), novel deep learning-based model optimized drone-based wildfire smoke detection. AMSO-SFS combines optical, infrared, Synthetic Aperture Radar (SAR) data detect under varied visibility conditions. The introduces (SFS-Conv) module enhance the discriminative capacity of features in both spatial frequency domains. Furthermore, utilizes weakly supervised learning adaptive scale angle identify regions minimal labeled data. Extensive experiments show that proposed outperforms current state-of-the-art (SoTA) models, achieving robust performance while maintaining computational efficiency, it suitable real-time drone deployment.

Language: Английский

Intelligent video-based fire detection: A novel dataset and real-time multi-stage classification approach DOI
Himani Sharma, Navdeep Kanwal

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126655 - 126655

Published: Jan. 1, 2025

Citations

0
AI-Driven UAV Surveillance for Agricultural Fire Safety DOI Creative Commons
Akmalbek Abdusalomov, Sabina Umirzakova,

Komil Tashev

et al.

Fire, Journal Year: 2025, Volume and Issue: 8(4), P. 142 - 142

Published: April 2, 2025

The increasing frequency and severity of agricultural fires pose significant threats to food security, economic stability, environmental sustainability. Traditional fire-detection methods, relying on satellite imagery ground-based sensors, often suffer from delayed response times high false-positive rates, limiting their effectiveness in mitigating fire-related damages. In this study, we propose an advanced deep learning-based framework that integrates the Single-Shot MultiBox Detector (SSD) with computationally efficient MobileNetV2 architecture. This integration enhances real-time fire- smoke-detection capabilities while maintaining a lightweight deployable model suitable for Unmanned Aerial Vehicle (UAV)-based monitoring. proposed was trained evaluated custom dataset comprising diverse fire scenarios, including various conditions intensities. Comprehensive experiments comparative analyses against state-of-the-art object-detection models, such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), SSD-based variants, demonstrated superior performance our model. results indicate approach achieves mean Average Precision (mAP) 97.7%, significantly surpassing conventional models detection speed 45 frames per second (fps) requiring only 5.0 GFLOPs computational power. These characteristics make it particularly deployment edge-computing environments, UAVs remote monitoring systems.

Language: Английский

Citations

0
Drone-Based Wildfire Detection with Multi-Sensor Integration DOI Creative Commons
Akmalbek Abdusalomov, Sabina Umirzakova,

Makhkamov Bakhtiyor Shukhratovich

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4651 - 4651

Published: Dec. 12, 2024

Wildfires pose a severe threat to ecological systems, human life, and infrastructure, making early detection critical for timely intervention. Traditional fire systems rely heavily on single-sensor approaches are often hindered by environmental conditions such as smoke, fog, or nighttime scenarios. This paper proposes Adaptive Multi-Sensor Oriented Object Detection with Space–Frequency Selective Convolution (AMSO-SFS), novel deep learning-based model optimized drone-based wildfire smoke detection. AMSO-SFS combines optical, infrared, Synthetic Aperture Radar (SAR) data detect under varied visibility conditions. The introduces (SFS-Conv) module enhance the discriminative capacity of features in both spatial frequency domains. Furthermore, utilizes weakly supervised learning adaptive scale angle identify regions minimal labeled data. Extensive experiments show that proposed outperforms current state-of-the-art (SoTA) models, achieving robust performance while maintaining computational efficiency, it suitable real-time drone deployment.

Language: Английский

Citations

2