دورية أكاديمية
Automatic Marine Debris Inspection
| العنوان: | Automatic Marine Debris Inspection |
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| المؤلفون: | Yu-Hsien Liao, Jih-Gau Juang |
| المصدر: | Aerospace, Vol 10, Iss 1, p 84 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Motor vehicles. Aeronautics. Astronautics |
| مصطلحات موضوعية: | object detection, convolutional neural network, model selection, model evaluation, hyperparameter tuning, UAV, Motor vehicles. Aeronautics. Astronautics, TL1-4050 |
| الوصف: | Plastic trash can be found anywhere, around the marina, beaches, and coastal areas in recent times. This study proposes a trash dataset called HAIDA and a trash detector that uses a YOLOv4-based object detection algorithm to monitor coastal trash pollution efficiently. Model selection, model evaluation, and hyperparameter tuning were applied to obtain the best model for the lowest generalization error in the real world. Comparison of the state-of-the-art object detectors based on YOLOv3, YOLOv4, and Scaled-YOLOv4 that used hyperparameter tuning, the three-way holdout method, and k-fold cross-validation have been presented. An unmanned aerial vehicle (UAV) was also employed to detect trash in coastal areas using the proposed method. The performance on image classification was satisfactory. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 10010084 2226-4310 |
| Relation: | https://www.mdpi.com/2226-4310/10/1/84; https://doaj.org/toc/2226-4310 |
| DOI: | 10.3390/aerospace10010084 |
| URL الوصول: | https://doaj.org/article/3f0507bf7851442e85661bb950f31cac |
| رقم الأكسشن: | edsdoj.3f0507bf7851442e85661bb950f31cac |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 10010084 22264310 |
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| DOI: | 10.3390/aerospace10010084 |