Development in building fire detection and evacuation system-a comprehensive review

Birajdar, Gajanand S.; Singh, Rajesh; Gehlot, Anita; Thakur, Amit Kumar

doi:10.11591/ijece.v10i6.pp6644-6654

Cited by 11 publications

(7 citation statements)

References 41 publications

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“…The next stage, we make bounding boxes on these grids and have a confidence value. Confidence value is the probability that the object is in the bounding box as in the (1). If the centroid of the fire object is in the grid cell, the grid is tasked with detecting the fire object.…”

Section: Transfer Learning With Yolo Methodsmentioning

confidence: 99%

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Comparison of two deep learning methods for detecting fire hotspots

Lestari

Kosasih

2022

IJECE

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Every high-rise building must meet construction requirements, i.e. it must have good safety to prevent unexpected events such as fire incident. To avoid the occurrence of a bigger fire, surveillance using closed circuit television (CCTV) videos is necessary. However, it is impossible for security forces to monitor for a full day. One of the methods that can be used to help security forces is deep learning method. In this study, we use two deep learning methods to detect fire hotspots, i.e. you only look once (YOLO) method and faster region-based convolutional neural network (faster R-CNN) method. The first stage, we collected 100 image data (70 training data and 30 test data). The next stage is model training which aims to make the model can recognize fire. Later, we calculate precision, recall, accuracy, and F1 score to measure performance of model. If the F1 score is close to 1, then the balance is optimal. In our experiment results, we found that YOLO has a precision is 100%, recall is 54.54%, accuracy is 66.67%, and F1 score is 0.70583667. While faster R-CNN has a precision is 87.5%, recall is 95.45%, accuracy is 86.67%, and F1 score is 0.913022.

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Section: Transfer Learning With Yolo Methodsmentioning

confidence: 99%

“…An area with a very large population causes building to be built vertically, i.e. high-rise building due to decrease empty land [1]. However, a high-rise building has been problems related to safe evacuate of occupant during emergencies such as fire.…”

Section: Introductionmentioning

confidence: 99%

Comparison of two deep learning methods for detecting fire hotspots

Lestari

Kosasih

2022

IJECE

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“…Some authors in [22]- [24] proposed a home automation prototype and mobile application framework using LoRa technology. Birajdar et al [25] proposes building fire detection methodology using LoRa communication protocol. The transmission bandwidth of a technique is called spread spectrum if it is greater than the minimum necessary information transmission bandwidth [26].…”

Section: Overview Of Lora Communication Technologymentioning

confidence: 99%

Performance evaluation of LoRa based sensor node and gateway architecture for oil pipeline management

Narayana

Singh

Gehlot

2022

IJECE

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These days, the oil industrial industry is leaning toward employing smart field improvements to streamline various activities in the midstream area. Oil transportation over large distances via pipelines has a cheap cost and high efficiency in this sector. If pipelines are not properly maintained, they may fail, potentially causing catastrophic, long-term, and irreversible consequences on both natural and human conditions. Low power wide area networks (LPWANs) are without a doubt one of the domains that cause the most from industrial fields when it comes to realizing the vision of the internet of things (IoT). Long-range (LoRa) is an emerging LPWAN technology that is particularly useful for transmitting data over long distances. The goal of this work is to offer a methodology for managing oil pipelines over long distances utilizing the LoRa communication protocol and the installation of sensor nodes and LoRa gateways along the pipeline. We also used the optimized network engineering tools (OPNET) simulator to examine various simulation findings of LoRa performance.

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“…In this final project, a smart home device with radio frequency was made using the Blynk application. This device is equipped with a radio frequency (RF) module to transmit sensor data, and can be used to control several rooms with different controllers but can also be controlled via a smartphone using the Blynk application [19,20]. So in this study, the relay will be connected to a 433Mhz radio frequency receiver module, and some equipment products that use 433Mhz radio frequency as communication can be connected to a 433Mhz radio frequency receiver.…”

Section: Introductionmentioning

confidence: 99%

Smart Home System With Radio Frequency Using Blynk Application

Dwiansyah

Prasetya

2022

emitor

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Over time, technology has been embedded in our daily lives and will develop more and more rapidly. Because everything can facilitate us in activities, especially in a house. But the problem arises how we manage and control electronic equipment efficiently and comfortably. In this study, an intelligent control system based on IoT (Internet of Things) technology has been suggested to solve the above problem, namely with a smart home control system based on IoT (Internet of Things) with a radio frequency of 433 MHz as a command to send and be forwarded to several receivers on each - each relay uses an IoT (Internet of Things) application, namely Blynk on a smartphone, it can also be controlled manually using a wireless button control and can be controlled remotely with a frequency of 433mhz. Through a series of control modules, such as switch modules, radio frequency control modules, you can directly control all kinds of household appliances. With smart phone can also communicate with smart center controller via wireless router via Wi-Fi interface. Because it has a radio frequency of 433 MHz as the bottom control layer, tools can be added to or removed from the control system very easily. And this control system functions as a switch that is connected to several receivers to each relay.

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Development in building fire detection and evacuation system-a comprehensive review

Cited by 11 publications

References 41 publications

Comparison of two deep learning methods for detecting fire hotspots

Comparison of two deep learning methods for detecting fire hotspots

Performance evaluation of LoRa based sensor node and gateway architecture for oil pipeline management

Smart Home System With Radio Frequency Using Blynk Application

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