دورية أكاديمية
MStrans: Multiscale Vision Transformer for Aerial Objects Detection
العنوان: | MStrans: Multiscale Vision Transformer for Aerial Objects Detection |
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المؤلفون: | Guanlin Lu, Xiaohui He, Qiang Wang, Faming Shao, Jinkang Wang, Likai Hao |
المصدر: | IEEE Access, Vol 10, Pp 75971-75985 (2022) |
بيانات النشر: | IEEE, 2022. |
سنة النشر: | 2022 |
المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering |
مصطلحات موضوعية: | Aerial object detection, deep learning, multiscale object detection, transformer, CNN, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
الوصف: | Detecting objects in aerial images is a challenging task due to the large-scale variations and arbitrary orientations with tiny instances. A new multi-scale transformer-based aerial objects detector called MStrans is proposed in this paper to deal with the challenges in aerial detection. To detect remote instances, MStrans adopts a multi-scale patch embedding transformer (MViT) to extract the global features of the image effectively. Furthermore, to capture the different discriminant features for classification and regression branch tasks, the partial interactive fusion module (PIFM) is designed to enhance the semantic expression of the key features of classification and regression tasks by using the strategy of interactive modeling of adjacent layer features. In addition, considering that the transformer may worsen the local feature details while capturing long-distance feature dependencies, this paper designs a global to local interactive fusion module (GLIFM). It uses the advantage of convolution to extract local features to enrich the detailed information in the transformer. Experiments were carried out on DOTA and DIOR datasets, and the MStrans achieves superior detection performances compared with other approaches. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 2169-3536 95252444 |
Relation: | https://ieeexplore.ieee.org/document/9826725/; https://doaj.org/toc/2169-3536 |
DOI: | 10.1109/ACCESS.2022.3190415 |
URL الوصول: | https://doaj.org/article/171da952524443f7bc0d1b79f71af10e |
رقم الأكسشن: | edsdoj.171da952524443f7bc0d1b79f71af10e |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 21693536 95252444 |
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DOI: | 10.1109/ACCESS.2022.3190415 |