ArticleSensors (Basel, Switzerland)2022
A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards Acoustic Bioindicator Integration in AI-Based Wildfire Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- A Multi-Stage Computer Vision Framework for Real-Time Fire Detection in Low-Light Multi-Camera Environments.Sensors (Basel, Switzerland) · 2026Article
- Edge-Friendly UAV Wildfire Smoke and Flame Detection Using Transfer Learning-Enhanced Lightweight Deep Learning Models.Sensors (Basel, Switzerland) · 2026Article
- An intelligent approach for early smoke/fire detection using vision sensors in smart cities.Scientific reports · 2026Article
- A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors.Sensors (Basel, Switzerland) · 2025Article
- Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection.Sensors (Basel, Switzerland) · 2025Article
- A Comparative Performance Evaluation of YOLO-Type Detectors on a New Open Fire and Smoke Dataset.Sensors (Basel, Switzerland) · 2024Article
- EarlySensors (Basel, Switzerland) · 2024Article
- Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques.Sensors (Basel, Switzerland) · 2024Article
- A Lightweight Cross-Layer Smoke-Aware Network.Sensors (Basel, Switzerland) · 2024Article
- FSH-DETR: An Efficient End-to-End Fire Smoke and Human Detection Based on a Deformable DEtection TRansformer (DETR).Sensors (Basel, Switzerland) · 2024Article
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- Unmanned aerial vehicles and pre-hospital emergency medicine.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Review
- Machine learning estimates on the impacts of detection times on wildfire suppression costs.PloS one · 2024Article
- Deep Learning Based Fire Risk Detection on Construction Sites.Sensors (Basel, Switzerland) · 2023Article
- An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images.Sensors (Basel, Switzerland) · 2023Article
- Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.Cancers · 2023Article
- Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches.Sensors (Basel, Switzerland) · 2023Article
- Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images.Sensors (Basel, Switzerland) · 2023Article
- A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network's backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset.
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