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COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION

This research focuses on improving the child detection system by utilizing AI with recent versions of pre-trained models as an alternative of using sensors existing in the child detection system. Currently the problems experienced is the sensors used in the market, to prevent child heatstroke in aut...

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Main Author: DON PEREZ, LIAP
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2019
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://ir.unimas.my/id/eprint/34410/1/COMPARISON%20OF%20CHILD%20DETECTION%20SYSTEM%20WITH%20ARTIFICIAL24pgs.pdf
http://ir.unimas.my/id/eprint/34410/5/COMPARISON%20OF%20CHILD%20DETECTION%20SYSTEM%20WITH%20ARTIFICIALft.pdf
http://ir.unimas.my/id/eprint/34410/
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spelling my.unimas.ir-344102024-11-25T02:10:13Z http://ir.unimas.my/id/eprint/34410/ COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION DON PEREZ, LIAP TJ Mechanical engineering and machinery This research focuses on improving the child detection system by utilizing AI with recent versions of pre-trained models as an alternative of using sensors existing in the child detection system. Currently the problems experienced is the sensors used in the market, to prevent child heatstroke in automobiles, cannot accurately determines the occupant in position and whether the person is an adult or child. Comparing pre-trained models of object detection with AI such as Single-Shot Detector MobileNet (SSD MobileNet), YOLO version 2 (YOLOv2), and YOLO version 3 (YOLOv3) could suggests a more accurate and precise child detection system. The system with three different object detection models were tested experimentally to evaluate the speed, accuracy and precision. At the end of experiments, it is founded that YOLO able to detect custom objects the fastest which is less than a second. Also, YOLOv3 Tiny GPU has the best average score of detection which is 100% at the first 80 cm while SSD MobileNet has its highest average score for detection at 70 cm. At the best distance, which is 70cm, SSD MobileNet shows an acceptable result since there is no false detection, while YOLO shows perfect reproducibility result at 70 cm. In conclusion, YOLOv3 is the most suitable model to improve the framework of the child detection system. Universiti Malaysia Sarawak, (UNIMAS) 2019 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/34410/1/COMPARISON%20OF%20CHILD%20DETECTION%20SYSTEM%20WITH%20ARTIFICIAL24pgs.pdf text en http://ir.unimas.my/id/eprint/34410/5/COMPARISON%20OF%20CHILD%20DETECTION%20SYSTEM%20WITH%20ARTIFICIALft.pdf DON PEREZ, LIAP (2019) COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
DON PEREZ, LIAP
COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION
description This research focuses on improving the child detection system by utilizing AI with recent versions of pre-trained models as an alternative of using sensors existing in the child detection system. Currently the problems experienced is the sensors used in the market, to prevent child heatstroke in automobiles, cannot accurately determines the occupant in position and whether the person is an adult or child. Comparing pre-trained models of object detection with AI such as Single-Shot Detector MobileNet (SSD MobileNet), YOLO version 2 (YOLOv2), and YOLO version 3 (YOLOv3) could suggests a more accurate and precise child detection system. The system with three different object detection models were tested experimentally to evaluate the speed, accuracy and precision. At the end of experiments, it is founded that YOLO able to detect custom objects the fastest which is less than a second. Also, YOLOv3 Tiny GPU has the best average score of detection which is 100% at the first 80 cm while SSD MobileNet has its highest average score for detection at 70 cm. At the best distance, which is 70cm, SSD MobileNet shows an acceptable result since there is no false detection, while YOLO shows perfect reproducibility result at 70 cm. In conclusion, YOLOv3 is the most suitable model to improve the framework of the child detection system.
format Final Year Project Report
author DON PEREZ, LIAP
author_facet DON PEREZ, LIAP
author_sort DON PEREZ, LIAP
title COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION
title_short COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION
title_full COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION
title_fullStr COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION
title_full_unstemmed COMPARISON OF CHILD DETECTION SYSTEM WITH ARTIFICIAL INTELLIGENCE USING MOBILENET, YOLOV2 AND YOLOV3 FOR OBJECT DETECTION
title_sort comparison of child detection system with artificial intelligence using mobilenet, yolov2 and yolov3 for object detection
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2019
url http://ir.unimas.my/id/eprint/34410/1/COMPARISON%20OF%20CHILD%20DETECTION%20SYSTEM%20WITH%20ARTIFICIAL24pgs.pdf
http://ir.unimas.my/id/eprint/34410/5/COMPARISON%20OF%20CHILD%20DETECTION%20SYSTEM%20WITH%20ARTIFICIALft.pdf
http://ir.unimas.my/id/eprint/34410/
_version_ 1817848745476227072
score 13.223943

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