Disaster Victims Detection System Using Convolutional Neural Network (CNN) Method

Hartawan, Dean Rizki; Purboyo, Tito Waluyo; Setianingsih, Casi

doi:10.1109/iciaict.2019.8784782

Cited by 51 publications

(24 citation statements)

References 12 publications

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“…The following are the test results based on the camera angle when shooting. The results of the CNN Algorithm for image processing cases get pretty good results like previous research that uses this algorithm also [6] - [8].…”

Section: Methodssupporting

confidence: 54%

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Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Pratama

Novianty

Setianingsih

2020

EECSI

Self Cite

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Lack of public awareness of health will cause serious problems. A small example, people now tend to always consume fatty foods without thinking about the risk of cholesterol levels in the body. Information on the level of cholesterol suffered by humans can be seen on the human eyelids. The eyelids, one part of the eye, can be known as a person's cholesterol level by observing the eyelids' shape and condition, but many people do not know about this. This application is an application made to detect cholesterol based on the shape of the eyelids. This can determine whether a person is exposed to cholesterol or not, using the Convolutional Neural Network (CNN) method in the classification process. This study provides an output in the form of early detection of cholesterol and prevention so that users can minimize the possibility of illness that will be suffered. This research was conducted to detect cholesterol one eyelid based on digital images. For detecting a cholesterol level, this system got 95.83% of accuracy.

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Section: Methodssupporting

confidence: 54%

“…Convolutional Neural Networks are one type of Deep Learning, often used to classify data such as image, sound, text, etc [6] - [8].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Pratama

Novianty

Setianingsih

2020

EECSI

Self Cite

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“…Image-based disaster victim detection is useful and can be integrated into advanced low-altitude UAVs for automatic victim search [34,35]. However, due to the lack of real victim datasets, existing victim detection systems [36][37][38] used common datasets such as INRIA person [39] and PASCAL VOC [14,40] for training. Moreover, these methods were tested on extremely small real datasets.…”

Section: Object Detection In Emergency Scenariosmentioning

confidence: 99%

“…The authors of [34] only used 19 images for testing. The authors of [36] tested their method using 50 images from the INRIA person dataset, which does not contain victim images. In this paper, we verify that detectors trained on the large popular dataset COCO [9] can not effectively detect real disaster victims because it contains regular human photos, which are quite different from the photos of victims in real disasters.…”

Section: Object Detection In Emergency Scenariosmentioning

confidence: 99%

Training a Disaster Victim Detection Network for UAV Search and Rescue Using Harmonious Composite Images

et al. 2022

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Human detection in images using deep learning has been a popular research topic in recent years and has achieved remarkable performance. Training a human detection network is useful for first responders to search for trapped victims in debris after a disaster. In this paper, we focus on the detection of such victims using deep learning, and we find that state-of-the-art detection models pre-trained on the well-known COCO dataset fail to detect victims. This is because all the people in the training set are shown in photos of daily life or sports activities, while people in the debris after a disaster usually only have parts of their bodies exposed. In addition, because of the dust, the colors of their clothes or body parts are similar to those of the surrounding debris. Compared with collecting images of common objects, images of disaster victims are extremely difficult to obtain for training. Therefore, we propose a framework to generate harmonious composite images for training. We first paste body parts onto a debris background to generate composite victim images and then use a deep harmonization network to make the composite images look more harmonious. We select YOLOv5l as the most suitable model, and experiments show that using composite images for training improves the AP (average precision) by 19.4% (15.3%→34.7%). Furthermore, using the harmonious images is of great benefit to training a better victim detector, and the AP is further improved by 10.2% (34.7%→44.9%). This research is part of the EU project INGENIOUS. Our composite images and code are publicly available on our website.

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“…Natural disasters are unpredictable events, Hartawan et al [ 9 ] enhanced multilayer perceptron algorithm by including convolutional neural network implemented on raspberry pi to find out the victims of natural disasters using streaming cameras and to aid the evacuation team to rescue the disaster victims. Amit et al [ 10 ] proposed applying automatic natural disaster detection to a convolutional neural network using the features of disaster from resized satellite images of landslide and flood detections.…”

Section: Related Workmentioning

confidence: 99%

Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Aamir

Ali

Irfan

et al. 2021

Sensors

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Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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Disaster Victims Detection System Using Convolutional Neural Network (CNN) Method

Cited by 51 publications

References 12 publications

Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Training a Disaster Victim Detection Network for UAV Search and Rescue Using Harmonious Composite Images

Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

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