Object Detection using YOLOv8 : A Systematic Review
This study is a Systematic Literature Review (SLR) that comprehensively reviews the recent advances in YOLOv8-based object detection models and their implementations in various application fields, such as UAV aerial photography, fruit ripeness identification, road defect detection, forest fire smoke...
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| Format: | Article |
| Language: | Indonesian |
| Published: |
Islamic University of Indragiri
2025-05-01
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| Series: | Sistemasi: Jurnal Sistem Informasi |
| Subjects: | |
| Online Access: | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5081 |
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| _version_ | 1849222083113910272 |
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| author | Nugraha Asthra Megantara Ema Utami |
| author_facet | Nugraha Asthra Megantara Ema Utami |
| author_sort | Nugraha Asthra Megantara |
| collection | DOAJ |
| description | This study is a Systematic Literature Review (SLR) that comprehensively reviews the recent advances in YOLOv8-based object detection models and their implementations in various application fields, such as UAV aerial photography, fruit ripeness identification, road defect detection, forest fire smoke detection, and medical imaging. This study evaluates the performance of YOLOv8 based on precision, recall, F1-score, and mean average precision (mAP) metrics, and compares its advantages and limitations with previous YOLO versions and other object detection algorithms. Improvements in the YOLOv8 architecture, including attention mechanisms, improved feature extraction, and hyperparameter optimization, enable significant improvements in accuracy and computational efficiency, especially for small objects and low-light conditions. In addition, the integration of image enhancement techniques strengthens the model's performance in challenging environmental conditions. This study is expected to be an important reference for researchers and practitioners in developing YOLOv8-based object detection models for real-world applications. |
| format | Article |
| id | doaj-art-69f875d9fed14ed58df3a9bb618933e2 |
| institution | Kabale University |
| issn | 2302-8149 2540-9719 |
| language | Indonesian |
| publishDate | 2025-05-01 |
| publisher | Islamic University of Indragiri |
| record_format | Article |
| series | Sistemasi: Jurnal Sistem Informasi |
| spelling | doaj-art-69f875d9fed14ed58df3a9bb618933e22025-08-26T08:05:47ZindIslamic University of IndragiriSistemasi: Jurnal Sistem Informasi2302-81492540-97192025-05-011431186119310.32520/stmsi.v14i3.50811089Object Detection using YOLOv8 : A Systematic ReviewNugraha Asthra Megantara0Ema Utami1Magister Informatika Universitas AMIKOM YogyakartaUniversitas AMIKOM YogyakartaThis study is a Systematic Literature Review (SLR) that comprehensively reviews the recent advances in YOLOv8-based object detection models and their implementations in various application fields, such as UAV aerial photography, fruit ripeness identification, road defect detection, forest fire smoke detection, and medical imaging. This study evaluates the performance of YOLOv8 based on precision, recall, F1-score, and mean average precision (mAP) metrics, and compares its advantages and limitations with previous YOLO versions and other object detection algorithms. Improvements in the YOLOv8 architecture, including attention mechanisms, improved feature extraction, and hyperparameter optimization, enable significant improvements in accuracy and computational efficiency, especially for small objects and low-light conditions. In addition, the integration of image enhancement techniques strengthens the model's performance in challenging environmental conditions. This study is expected to be an important reference for researchers and practitioners in developing YOLOv8-based object detection models for real-world applications.https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5081yolov8object detectionsystematic reviewobject recognitioncomputer vision |
| spellingShingle | Nugraha Asthra Megantara Ema Utami Object Detection using YOLOv8 : A Systematic Review Sistemasi: Jurnal Sistem Informasi yolov8 object detection systematic review object recognition computer vision |
| title | Object Detection using YOLOv8 : A Systematic Review |
| title_full | Object Detection using YOLOv8 : A Systematic Review |
| title_fullStr | Object Detection using YOLOv8 : A Systematic Review |
| title_full_unstemmed | Object Detection using YOLOv8 : A Systematic Review |
| title_short | Object Detection using YOLOv8 : A Systematic Review |
| title_sort | object detection using yolov8 a systematic review |
| topic | yolov8 object detection systematic review object recognition computer vision |
| url | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5081 |
| work_keys_str_mv | AT nugrahaasthramegantara objectdetectionusingyolov8asystematicreview AT emautami objectdetectionusingyolov8asystematicreview |