ArticleSensors (Basel, Switzerland)2022
Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People.
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 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital accessibility in the era of artificial intelligence-Bibliometric analysis and systematic review.Frontiers in artificial intelligence · 2024Pooled it
- 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
- EarlySensors (Basel, Switzerland) · 2024Article
- Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques.Sensors (Basel, Switzerland) · 2024Article
- A Smart Cane Based on 2D LiDAR and RGB-D Camera Sensor-Realizing Navigation and Obstacle Recognition.Sensors (Basel, Switzerland) · 2024Article
- Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network.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
- Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent.Sensors (Basel, Switzerland) · 2023Article
- A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments.Sensors (Basel, Switzerland) · 2023Article
- Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms.Sensors (Basel, Switzerland) · 2023Review
- An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture.Sensors (Basel, Switzerland) · 2022Article
- A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5.Sensors (Basel, Switzerland) · 2022Article
- Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces.Sensors (Basel, Switzerland) · 2022Article
- Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization.Sensors (Basel, Switzerland) · 2022Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible. In this study, to reduce the loss of human lives and property damage, we introduce the development of the vision-based early flame recognition and notification approach using artificial intelligence for assisting BVI people. The proposed fire alarm control system for indoor buildings can provide accurate information on fire scenes. In our proposed method, all the processes performed manually were automated, and the performance efficiency and quality of fire classification were improved. To perform real-time monitoring and enhance the detection accuracy of indoor fire disasters, the proposed system uses the YOLOv5m model, which is an updated version of the traditional YOLOv5. The experimental results show that the proposed system successfully detected and notified the occurrence of catastrophic fires with high speed and accuracy at any time of day or night, regardless of the shape or size of the fire. Finally, we compared the competitiveness level of our method with that of other conventional fire-detection methods to confirm the seamless classification results achieved using performance evaluation matrices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.