Attention Based CNN model for Fire Detection and Localization in Real-World Images

2021 ◽  
pp. 116114
Author(s):  
Saima Majid ◽  
Fayadh Alenezi ◽  
Sarfaraz Masood ◽  
Musheer Ahmad ◽  
Emine Selda Gündüz ◽  
...  
Keyword(s):  
Real World ◽  
Fire Detection ◽  
Detection And Localization

scholarly journals Vision Sensor-Based Real-Time Fire Detection in Resource-Constrained IoT Environments

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hikmat Yar ◽  
Tanveer Hussain ◽  
Zulfiqar Ahmad Khan ◽  
Deepika Koundal ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
False Positive Rate ◽  
Fire Detection ◽  
Resource Constrained ◽  
Training Time ◽  
Input Size ◽  
Proposed Model ◽  
High Level ◽  
Early Fire Detection

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.

False Fire Alarm Detection Using Data Mining Techniques

2020 ◽  
Vol 12 (4) ◽  
pp. 21-35
Author(s):  
Raheel Zafar ◽  
Shah Zaib ◽  
Muhammad Asif
Keyword(s):  
Real World ◽  
Emergency Services ◽  
Research Work ◽  
Fire Detection ◽  
Smart Homes ◽  
Early Warning Systems ◽  
False Alarms ◽  
Real World Data ◽  
Fire Alarm ◽  
Fire Alarm System

In the era of smart home technology, early warning systems and emergency services are inevitable. To make smart homes safer, early fire alarm systems can play a significant role. Smart homes usually utilize communication, sensors, actuators, and other technologies to provide a safe and smart environment. This research work introduced a model for the fire alarm system and designed a fire alarm detection (FAD) simulator to produce a synthetic dataset. The designed simulator utilizes a variety of sensors (temperature, gas, and humidity) to simulate fire alarm scenarios based on real-world data. The produced data is investigated and analyzed to classify the possible fire behaviors based on key assumptions taken from real-world scenarios. Different classification models are used to determine an optimal classifier for fire detection. The proposed technique can identify the false alarms based on parameters like temperature, smoke, and gas values of different sensors embedded in a fire alarm detection simulator.

scholarly journals Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications

2019 ◽  
Vol 49 (7) ◽  
pp. 1419-1434 ◽  
Author(s):  
Khan Muhammad ◽  
Jamil Ahmad ◽  
Zhihan Lv ◽  
Paolo Bellavista ◽  
Po Yang ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Fire Detection ◽  
Deep Cnn ◽  
Detection And Localization ◽  
Surveillance Applications
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