ArticleSensors (Basel, Switzerland)2022
Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired.
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 27 papers.
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Who cites it
27 citing papers in PubMed.
- Assistive Mobile Application for Fire Emergency Evacuation of Visually Impaired People.Sensors (Basel, Switzerland) · 2026Article
- Enhanced YOLOv7 with CDP-ELAN and gather-distribute mechanism for robust smoke and flame detection.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
- PC-CS-YOLO: High-Precision Obstacle Detection for Visually Impaired Safety.Sensors (Basel, Switzerland) · 2025Article
- Federated learning based fire detection method using local MobileNet.Scientific reports · 2024Article
- Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques.Sensors (Basel, Switzerland) · 2024Article
- Firefighting Water Jet Trajectory Detection from Unmanned Aerial Vehicle Imagery Using Learnable Prompt Vectors.Sensors (Basel, Switzerland) · 2024Article
- YOLOFM: an improved fire and smoke object detection algorithm based on YOLOv5n.Scientific reports · 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
- 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
- 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
- Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People.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
- An Algorithm with Iteration Uncertainty Eliminate Based on Geomagnetic Fingerprint under Mobile Edge Computing for Indoor Localization.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The growing aging population suffers from high levels of vision and cognitive impairment, often resulting in a loss of independence. Such individuals must perform crucial everyday tasks such as cooking and heating with systems and devices designed for visually unimpaired individuals, which do not take into account the needs of persons with visual and cognitive impairment. Thus, the visually impaired persons using them run risks related to smoke and fire. In this paper, we propose a vision-based fire detection and notification system using smart glasses and deep learning models for blind and visually impaired (BVI) people. The system enables early detection of fires in indoor environments. To perform real-time fire detection and notification, the proposed system uses image brightness and a new convolutional neural network employing an improved YOLOv4 model with a convolutional block attention module. The h-swish activation function is used to reduce the running time and increase the robustness of YOLOv4. We adapt our previously developed smart glasses system to capture images and inform BVI people about fires and other surrounding objects through auditory messages. We create a large fire image dataset with indoor fire scenes to accurately detect fires. Furthermore, we develop an object mapping approach to provide BVI people with complete information about surrounding objects and to differentiate between hazardous and nonhazardous fires. The proposed system shows an improvement over other well-known approaches in all fire detection metrics such as precision, recall, and average precision.
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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.