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Comparison of different deep learning object detection algorithms on fruit drying characterization

Object detection is an essential task in the field of computer vision and a prominent area of research. In the past, the categorization of raw and dry Tamanu fruits was dependent on human perception. Nevertheless, due to the progress in object detection, this task can currently be computerized. This...

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Main Authors: Umair, Mohammad Yamin, Norazlianie, Sazali, Kettner, Maurice, Mohd Azraai, Mohd Razman, Weiβ, Robert
Format: Article
Language:English
Published: Semarak Ilmu Sdn. Bhd. 2024
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/43567/1/Comparison%20of%20different%20deep%20learning%20object%20detection%20algorithms%20on%20fruit%20drying%20characterization.pdf
http://umpir.ump.edu.my/id/eprint/43567/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/12160
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spelling my.ump.umpir.435672025-01-12T10:13:01Z http://umpir.ump.edu.my/id/eprint/43567/ Comparison of different deep learning object detection algorithms on fruit drying characterization Umair, Mohammad Yamin Norazlianie, Sazali Kettner, Maurice Mohd Azraai, Mohd Razman Weiβ, Robert TS Manufactures Object detection is an essential task in the field of computer vision and a prominent area of research. In the past, the categorization of raw and dry Tamanu fruits was dependent on human perception. Nevertheless, due to the progress in object detection, this task can currently be computerized. This study employs three deep learning object detection models: You Only Look Once v5m (YOLOv5m), Single Shot Detector (SSD) MobileNet and EfficientDet. The models were trained using images of Tamanu fruits in their raw and dry state, which were directly collected from the dryer device. Following the completion of training, the models underwent evaluation to identify the one with the highest level of accuracy. YOLOv5m demonstrated superior performance compared to SSD MobileNet and EfficientDet, achieving a mean average precision (mAP) of 0.99589. SSD MobileNet demonstrated exceptional performance in real-time object detection, accurately detecting the majority of objects with a high level of confidence. This study showcases the efficacy of employing deep learning object detection models to automate the classification of Tamanu fruit. Semarak Ilmu Sdn. Bhd. 2024 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/43567/1/Comparison%20of%20different%20deep%20learning%20object%20detection%20algorithms%20on%20fruit%20drying%20characterization.pdf Umair, Mohammad Yamin and Norazlianie, Sazali and Kettner, Maurice and Mohd Azraai, Mohd Razman and Weiβ, Robert (2024) Comparison of different deep learning object detection algorithms on fruit drying characterization. Journal of Advanced Research in Applied Sciences and Engineering Technology. pp. 1-14. ISSN 2462-1943. (In Press / Online First) (In Press / Online First) https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/12160
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TS Manufactures
spellingShingle TS Manufactures
Umair, Mohammad Yamin
Norazlianie, Sazali
Kettner, Maurice
Mohd Azraai, Mohd Razman
Weiβ, Robert
Comparison of different deep learning object detection algorithms on fruit drying characterization
description Object detection is an essential task in the field of computer vision and a prominent area of research. In the past, the categorization of raw and dry Tamanu fruits was dependent on human perception. Nevertheless, due to the progress in object detection, this task can currently be computerized. This study employs three deep learning object detection models: You Only Look Once v5m (YOLOv5m), Single Shot Detector (SSD) MobileNet and EfficientDet. The models were trained using images of Tamanu fruits in their raw and dry state, which were directly collected from the dryer device. Following the completion of training, the models underwent evaluation to identify the one with the highest level of accuracy. YOLOv5m demonstrated superior performance compared to SSD MobileNet and EfficientDet, achieving a mean average precision (mAP) of 0.99589. SSD MobileNet demonstrated exceptional performance in real-time object detection, accurately detecting the majority of objects with a high level of confidence. This study showcases the efficacy of employing deep learning object detection models to automate the classification of Tamanu fruit.
format Article
author Umair, Mohammad Yamin
Norazlianie, Sazali
Kettner, Maurice
Mohd Azraai, Mohd Razman
Weiβ, Robert
author_facet Umair, Mohammad Yamin
Norazlianie, Sazali
Kettner, Maurice
Mohd Azraai, Mohd Razman
Weiβ, Robert
author_sort Umair, Mohammad Yamin
title Comparison of different deep learning object detection algorithms on fruit drying characterization
title_short Comparison of different deep learning object detection algorithms on fruit drying characterization
title_full Comparison of different deep learning object detection algorithms on fruit drying characterization
title_fullStr Comparison of different deep learning object detection algorithms on fruit drying characterization
title_full_unstemmed Comparison of different deep learning object detection algorithms on fruit drying characterization
title_sort comparison of different deep learning object detection algorithms on fruit drying characterization
publisher Semarak Ilmu Sdn. Bhd.
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/43567/1/Comparison%20of%20different%20deep%20learning%20object%20detection%20algorithms%20on%20fruit%20drying%20characterization.pdf
http://umpir.ump.edu.my/id/eprint/43567/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/12160
_version_ 1822924940539592704
score 13.243885

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