A Human Detection Approach for Burning Building Sites Using Deep Learning Techniques

Jaradat, Farah B.; Valles, Damian

doi:10.1109/csci46756.2018.00277

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…It is a hazard that we would want to not only prevent in advance, but also to respond to quickly in terms of emergency response. An effective fire evacuation plan is important to save human lives, protect firefighters, and minimize property loss [ 1 ]. We will focus on the application of the YOLOv4 model with a thermal camera following the National Fire Protection Association (NFPA) 1801 [ 2 ] standards to detect humans in a smoky fire scene during an emergency evacuation.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning with Thermal Imaging for Human Detection in Heavy Smoke Scenarios

Tsai

Liao

Yuan

2022

Sensors

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In this study, we propose using a thermal imaging camera (TIC) with a deep learning model as an intelligent human detection approach during emergency evacuations in a low-visibility smoky fire scenarios. We use low-wavelength infrared (LWIR) images taken by a TIC qualified with the National Fire Protection Association (NFPA) 1801 standards as input to the YOLOv4 model for real-time object detection. The model trained with a single Nvidia GeForce 2070 can achieve >95% precision for the location of people in a low-visibility smoky scenario with 30.1 frames per second (FPS). This real-time result can be reported to control centers as useful information to help provide timely rescue and provide protection to firefighters before entering dangerous smoky fire situations.

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Section: Introductionmentioning

confidence: 99%

Using Deep Learning with Thermal Imaging for Human Detection in Heavy Smoke Scenarios

Tsai

Liao

Yuan

2022

Sensors

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Efficient Detection of Humans in Flames Using HOG as a Feature Criterion in Machine Learning

Kumar

2022

International Journal of Distributed Artificial Intelligence

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Detection of humans in flames is a challenging task. The task in this work is classified into two stages. The first is detection of fire, and the second is detection of human. The proposed method involves fire detection based on colour format YCbCr for image preprocessing. It further uses a histogram of oriented gradient (HOG) and support vector machine (SVM) to detect a human in the fire. It evaluates several motion-based feature sets for human detection in the form of videos. In this work, both modules were integrated to make them work together. For the detection of fire, four different rules involving colour thresholding were used and background differencing was used for moving object detection. The main objective of this work is to spot the humans in the flames who are trapped in it so they can be rescued quickly. This can help the firefighters in rapid planning and serious zone detection. The proposed model has 81% efficiency, which has outperformed the existing models for detection of humans in flames.

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A Human Detection Approach for Burning Building Sites Using Deep Learning Techniques

Cited by 2 publications

References 6 publications

Using Deep Learning with Thermal Imaging for Human Detection in Heavy Smoke Scenarios

Using Deep Learning with Thermal Imaging for Human Detection in Heavy Smoke Scenarios

Efficient Detection of Humans in Flames Using HOG as a Feature Criterion in Machine Learning

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