ArticleSensors (Basel, Switzerland)2023
An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
- DustVeil: Label-Free Real-Time Detection of Airborne Coal-Mine Dust in Camera Streams via Physically-Grounded Multi-Cue Fusion and Knowledge Distillation.Sensors (Basel, Switzerland) · 2026Article
- BGC-LiteNet: BeiDou grid code embedded lightweight neural architecture for real-time UAV fire detection and localization.Scientific reports · 2026Article
- MmodalFire: A Continuous Multimodal Dataset Comprising Video and Physical Sensing Data for Detecting Indoor Fires.Scientific data · 2026Article
- Research and optimization of a multilevel fire detection framework based on deep learning and classical pattern recognition techniques.Scientific reports · 2025Article
- An advanced fire detection system for assisting visually challenged people using recurrent neural network and sea-horse optimizer algorithm.Scientific reports · 2025Article
- Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection.Sensors (Basel, Switzerland) · 2025Article
- Dairy DigiD: a keypoint-based deep learning system for classifying dairy cattle by physiological and reproductive status.Frontiers in artificial intelligence · 2025Article
- Federated learning based fire detection method using local MobileNet.Scientific reports · 2024Article
- A Comparative Performance Evaluation of YOLO-Type Detectors on a New Open Fire and Smoke Dataset.Sensors (Basel, Switzerland) · 2024Article
- Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques.Sensors (Basel, Switzerland) · 2024Article
- An Improved Fire and Smoke Detection Method Based on YOLOv8n for Smart Factories.Sensors (Basel, Switzerland) · 2024Article
- A Smart Visual Sensor for Smoke Detection Based on Deep Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.AIMS public health · 2024Article
- Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping.Frontiers in plant science · 2024Article
- Deep learningJournal of biomedical optics · 2024Article
- Early Fire Detection Using Long Short-Term Memory-Based Instance Segmentation and Internet of Things for Disaster Management.Sensors (Basel, Switzerland) · 2023Article
- An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images.Sensors (Basel, Switzerland) · 2023Article
- Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches.Sensors (Basel, Switzerland) · 2023Article
- Advancements in Forest Fire Prevention: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2023Review
- Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
With an increase in both global warming and the human population, forest fires have become a major global concern. This can lead to climatic shifts and the greenhouse effect, among other adverse outcomes. Surprisingly, human activities have caused a disproportionate number of forest fires. Fast detection with high accuracy is the key to controlling this unexpected event. To address this, we proposed an improved forest fire detection method to classify fires based on a new version of the Detectron2 platform (a ground-up rewrite of the Detectron library) using deep learning approaches. Furthermore, a custom dataset was created and labeled for the training model, and it achieved higher precision than the other models. This robust result was achieved by improving the Detectron2 model in various experimental scenarios with a custom dataset and 5200 images. The proposed model can detect small fires over long distances during the day and night. The advantage of using the Detectron2 algorithm is its long-distance detection of the object of interest. The experimental results proved that the proposed forest fire detection method successfully detected fires with an improved precision of 99.3%.
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