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Segmentation to Detect the Presence of Human during Natural Disasters

Chethana H. T*, Pooja C. A**, P. Vamshika***

*-*** Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.

Periodicity:July - September'2019
DOI : https://doi.org/10.26634/jdp.7.3.16934

Abstract

Nowadays, object detection is widely used for various purposes. There is a need for detecting living objects so that various problems like humans getting stuck in an undesirable place, and to find the suspected objects out of a group of other objects. Image segmentation is a process of dividing a digital picture into many segments, which helps in getting meaningful and well analyzed data that can be used in obtaining a well-defined output. Detection of living beings during the natural disasters is the most challenging task. Our proposed system makes use of You Only Look Once (YOLOv3), the quickest and most accurate among object detection system. During natural disasters, objects/living beings are detected using YOLOv3 model and the same information will be sent to the rescue team so that lives of human beings will be saved quickly and effectively.

Keywords

Convolutional Neural Network, Mask-RCNN, Image Segmentation, Feature MAP, Object Detection.

How to Cite this Article?

Chethana, H. T., Pooja, C. A., and Vamshika, P. (2019). Segmentation to Detect the Presence of Human during Natural Disasters. i-manager’s Journal on Digital Signal Processing. 7(3), 21-26. https://doi.org/10.26634/jdp.7.3.16934

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