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Comparative analysis of deep learning models for sheep detection in aerial imagery

This research evaluates You Look Only Once - YOLOv5, YOLO-NAS, and Detection Transformer (DETR) and provides a thorough evaluation of deep learning models for sheep identification in aerial pictures. A carefully selected collection of 4,212 aerial photos of sheep in various environments was used to...

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Main Authors: Ismail, Muhammad Syahmie, Samad, Rosdiyana, Pebrianti, Dwi, Mustafa, Mahfuzah, Abdullah, Nor Rul Hasma
Format: Proceeding Paper
Language:English
English
Published: IEEE 2024
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/115359/7/115359_Comparative_Analysis_of_Deep_Learning_Models_for_Sheep_Detection_in_Aerial_Imagery.pdf
http://irep.iium.edu.my/115359/13/115359_Comparative%20Analysis%20of%20Deep%20Learning%20Models%20for%20Sheep%20Detection%20in%20Aerial%20Imagery_Scopus.pdf
http://irep.iium.edu.my/115359/
https://ieeexplore-ieee-org.ezlib.iium.edu.my/document/10652292
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spelling my.iium.irep.1153592024-10-29T07:45:41Z http://irep.iium.edu.my/115359/ Comparative analysis of deep learning models for sheep detection in aerial imagery Ismail, Muhammad Syahmie Samad, Rosdiyana Pebrianti, Dwi Mustafa, Mahfuzah Abdullah, Nor Rul Hasma T Technology (General) This research evaluates You Look Only Once - YOLOv5, YOLO-NAS, and Detection Transformer (DETR) and provides a thorough evaluation of deep learning models for sheep identification in aerial pictures. A carefully selected collection of 4,212 aerial photos of sheep in various environments was used to thoroughly evaluate model performance. The implementation involved preprocessing, augmentation, model parameter optimization, training on Google Collab GPU s, and quantitative test results analysis. Important results show that on the sheep dataset, YOLOv5 and YOLO-NAS achieved an impressive accuracy of 97%, exceeding DETR's initial accuracy range of 70-80%. However, after adjusting the hyperparameters, DETR's accuracy significantly increased to 86%, showing less overfitting and more stability. The increased accuracy of YOLO models highlights how useful they are for sheep counting and aerial surveillance to support modern farming techniques. However, improvements to the transformer based DETR may increase its usefulness even more. This research offers valuable insights into the real-world applications of deep learning for livestock detection in aerial imagery, providing a foundation for future advancements in the field. IEEE 2024-08 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/115359/7/115359_Comparative_Analysis_of_Deep_Learning_Models_for_Sheep_Detection_in_Aerial_Imagery.pdf application/pdf en http://irep.iium.edu.my/115359/13/115359_Comparative%20Analysis%20of%20Deep%20Learning%20Models%20for%20Sheep%20Detection%20in%20Aerial%20Imagery_Scopus.pdf Ismail, Muhammad Syahmie and Samad, Rosdiyana and Pebrianti, Dwi and Mustafa, Mahfuzah and Abdullah, Nor Rul Hasma (2024) Comparative analysis of deep learning models for sheep detection in aerial imagery. In: 2024 9th International Conference on Mechatronics Engineering (ICOM), 13-14 August 2024, Kulliyyah of Engineering, IIUM. https://ieeexplore-ieee-org.ezlib.iium.edu.my/document/10652292 10.1109/ICOM61675.2024.10652292
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Ismail, Muhammad Syahmie
Samad, Rosdiyana
Pebrianti, Dwi
Mustafa, Mahfuzah
Abdullah, Nor Rul Hasma
Comparative analysis of deep learning models for sheep detection in aerial imagery
description This research evaluates You Look Only Once - YOLOv5, YOLO-NAS, and Detection Transformer (DETR) and provides a thorough evaluation of deep learning models for sheep identification in aerial pictures. A carefully selected collection of 4,212 aerial photos of sheep in various environments was used to thoroughly evaluate model performance. The implementation involved preprocessing, augmentation, model parameter optimization, training on Google Collab GPU s, and quantitative test results analysis. Important results show that on the sheep dataset, YOLOv5 and YOLO-NAS achieved an impressive accuracy of 97%, exceeding DETR's initial accuracy range of 70-80%. However, after adjusting the hyperparameters, DETR's accuracy significantly increased to 86%, showing less overfitting and more stability. The increased accuracy of YOLO models highlights how useful they are for sheep counting and aerial surveillance to support modern farming techniques. However, improvements to the transformer based DETR may increase its usefulness even more. This research offers valuable insights into the real-world applications of deep learning for livestock detection in aerial imagery, providing a foundation for future advancements in the field.
format Proceeding Paper
author Ismail, Muhammad Syahmie
Samad, Rosdiyana
Pebrianti, Dwi
Mustafa, Mahfuzah
Abdullah, Nor Rul Hasma
author_facet Ismail, Muhammad Syahmie
Samad, Rosdiyana
Pebrianti, Dwi
Mustafa, Mahfuzah
Abdullah, Nor Rul Hasma
author_sort Ismail, Muhammad Syahmie
title Comparative analysis of deep learning models for sheep detection in aerial imagery
title_short Comparative analysis of deep learning models for sheep detection in aerial imagery
title_full Comparative analysis of deep learning models for sheep detection in aerial imagery
title_fullStr Comparative analysis of deep learning models for sheep detection in aerial imagery
title_full_unstemmed Comparative analysis of deep learning models for sheep detection in aerial imagery
title_sort comparative analysis of deep learning models for sheep detection in aerial imagery
publisher IEEE
publishDate 2024
url http://irep.iium.edu.my/115359/7/115359_Comparative_Analysis_of_Deep_Learning_Models_for_Sheep_Detection_in_Aerial_Imagery.pdf
http://irep.iium.edu.my/115359/13/115359_Comparative%20Analysis%20of%20Deep%20Learning%20Models%20for%20Sheep%20Detection%20in%20Aerial%20Imagery_Scopus.pdf
http://irep.iium.edu.my/115359/
https://ieeexplore-ieee-org.ezlib.iium.edu.my/document/10652292
_version_ 1814932539227242496
score 13.211869

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