@article{ref1,
title="RETRACTED: Vision sensor-based real-time fire detection in resource-constrained IoT environments",
journal="Computational intelligence and neuroscience",
year="2021",
author="Yar, Hikmat and Hussain, Tanveer and Khan, Zulfiqar Ahmad and Koundal, Deepika and Lee, Mi Young and Baik, Sung Wook",
volume="2021",
number="",
pages="e5195508-e5195508",
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.<p /> <p>Language: en</p>",
language="en",
issn="1687-5265",
doi="10.1155/2021/5195508",
url="http://dx.doi.org/10.1155/2021/5195508"
}