ArticleComputational intelligence and neuroscience2021
Vision Sensor-Based Real-Time Fire Detection in Resource-Constrained IoT Environments.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- An efficient smart phone application for wheat crop diseases detection using advanced machine learning.PloS one · 2025Article
- The intelligent evaluation in ice and snow tourism based on LSTM network.Scientific reports · 2024Article
- Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition.Sensors (Basel, Switzerland) · 2023Article
- EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism.Sensors (Basel, Switzerland) · 2023Article
- An IoT Enable Anomaly Detection System for Smart City Surveillance.Sensors (Basel, Switzerland) · 2023Article
- Solar Power Prediction Using Dual Stream CNN-LSTM Architecture.Sensors (Basel, Switzerland) · 2023Article
- Retracted: Vision Sensor-Based Real-Time Fire Detection in Resource-Constrained IoT Environments.Computational intelligence and neuroscience · 2023Article
- An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity.Sensors (Basel, Switzerland) · 2022Article
- Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM.Sensors (Basel, Switzerland) · 2022Article
- Facial Emotion Recognition in Verbal Communication Based on Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer.Sensors (Basel, Switzerland) · 2022Article
- An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection.Sensors (Basel, Switzerland) · 2022Article
- Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-12-13Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real-time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challenges, early fire detection and automatic response are very significant. Therefore, we develop a novel framework based on a lightweight convolutional neural network (CNN), requiring less training time, and it is applicable over resource-constrained devices. The internal architecture of the proposed model is inspired by the block-wise VGG16 architecture with a significantly reduced number of parameters, input size, inference time, and comparatively higher accuracy for early fire detection. In the proposed model, small-size uniform convolutional filters are employed that are specifically designed to capture fine details of input fire images with a sequentially increasing number of channels to aid effective feature extraction. The proposed model is evaluated on two datasets such as a benchmark Foggia's dataset and our newly created small-scaled fire detection dataset with extremely challenging real-world images containing a high-level of diversity. Experimental results conducted on both datasets reveal the better performance of the proposed model compared to state-of-the-art in terms of accuracy, false-positive rate, model size, and running time, which indicates its robustness and feasible installation in real-world scenarios.
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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.