دورية أكاديمية
Oriented object detection in satellite images using convolutional neural network based on ResNeXt
| العنوان: | Oriented object detection in satellite images using convolutional neural network based on ResNeXt |
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| المؤلفون: | Asep Haryono, Grafika Jati, Wisnu Jatmiko |
| المصدر: | ETRI Journal, Pp 46-2 (2024) |
| بيانات النشر: | Electronics and Telecommunications Research Institute (ETRI), 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Telecommunication LCC:Electronics |
| مصطلحات موضوعية: | box-boundary-aware vector, convolutional neural network, oriented object detection, resnext101, satellite imagery, Telecommunication, TK5101-6720, Electronics, TK7800-8360 |
| الوصف: | Most object detection methods use a horizontal bounding box that causes problems between adjacent objects with arbitrary directions, resulting in misaligned detection. Hence, the horizontal anchor should be replaced by a rotating anchor to determine oriented bounding boxes. A two-stage process of delineating a horizontal bounding box and then converting it into an oriented bounding box is inefficient. To improve detection, a box-boundary-aware vector can be estimated based on a convolutional neural network. Specifically, we propose a ResNeXt101 encoder to overcome the weaknesses of the conven-tional ResNet, which is less effective as the network depth and complexity increase. Owing to the cardinality of using a homogeneous design and multi-branch architecture with few hyperparameters, ResNeXt captures better information than ResNet. Experimental results demonstrate more accurate and faster oriented object detection of our proposal compared with a baseline, achieving a mean average precision of 89.41% and inference rate of 23.67 fps. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1225-6463 2233-7326 |
| Relation: | https://doaj.org/toc/1225-6463; https://doaj.org/toc/2233-7326 |
| DOI: | 10.4218/etrij.2022-0446 |
| URL الوصول: | https://doaj.org/article/3e329c7c22ba4cc49b5c7683d0084a3b |
| رقم الأكسشن: | edsdoj.3e329c7c22ba4cc49b5c7683d0084a3b |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 12256463 22337326 |
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| DOI: | 10.4218/etrij.2022-0446 |