التفاصيل البيبلوغرافية
| العنوان: |
Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection |
| المؤلفون: |
Sirisha, U., Praveen, S. Phani, Srinivasu, Parvathaneni Naga, Barsocchi, Paolo, Bhoi, Akash Kumar |
| المصدر: |
International Journal of Computational Intelligence Systems ; volume 16, issue 1 ; ISSN 1875-6883 |
| بيانات النشر: |
Springer Science and Business Media LLC |
| سنة النشر: |
2023 |
| مصطلحات موضوعية: |
Computational Mathematics, General Computer Science |
| الوصف: |
Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| DOI: |
10.1007/s44196-023-00302-w |
| DOI: |
10.1007/s44196-023-00302-w.pdf |
| DOI: |
10.1007/s44196-023-00302-w/fulltext.html |
| الإتاحة: |
https://doi.org/10.1007/s44196-023-00302-wTest |
| حقوق: |
https://creativecommons.org/licenses/by/4.0Test ; https://creativecommons.org/licenses/by/4.0Test |
| رقم الانضمام: |
edsbas.E74DE470 |
| قاعدة البيانات: |
BASE |
