当前位置: X-MOL 学术IEEE Trans. Geosci. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
CLT-Det: Correlation Learning Based on Transformer for Detecting Dense Objects in Remote Sensing Images
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2022-09-06 , DOI: 10.1109/tgrs.2022.3204770
Yong Zhou 1 , Silin Chen 1 , Jiaqi Zhao 2 , Rui Yao 1 , Yong Xue 3 , Abdulmotaleb El Saddik 4
Affiliation  

Challenges still exist in the task of object detection in remote sensing images with densely distributed objects due to large variation in scale and neglect of the relative position and correlation. To address these issues, a correlation learning detector based on transformer (CLT-Det) is proposed for detecting dense objects in remote sensing images. A transformer attention module (TAM) is designed to improve the densely packed objects’ model representation ability by learning pixelwise attention with a transformer. To alleviate the semantic gap caused by the variations in scale, a feature refinement module (FRM) is proposed by improving the multiscale feature pyramid. A correlation transformer module (CTM) is proposed to extract correlation information and it encodes position information of dense objects’ features on the classification branch for fully using the position information and correlation among objects. Extensive experiments compared with several state-of-art methods on two challenging remote sensing datasets, namely, dataset for object detection in aerial images (DOTA) and HRSC2016, demonstrate that the proposed CLT-Det achieves promising and competitive performance.

中文翻译:

CLT-Det:基于 Transformer 的相关学习,用于检测遥感图像中的密集对象

由于尺度变化大,忽略了相对位置和相关性,在目标分布密集的遥感图像中,目标检测任务仍然存在挑战。为了解决这些问题,提出了一种基于变换器的相关学习检测器(CLT-Det),用于检测遥感图像中的密集对象。变压器注意模块 (TAM) 旨在通过使用变压器学习像素级注意来提高密集对象的模型表示能力。为了缓解由尺度变化引起的语义差距,通过改进多尺度特征金字塔提出了特征细化模块(FRM)。提出了一种相关变换模块(CTM)来提取相关信息,并将密集对象特征的位置信息编码在分类分支上,以充分利用对象之间的位置信息和相关性。在两个具有挑战性的遥感数据集(即航空图像中目标检测数据集 (DOTA) 和 HRSC2016)上与几种最先进的方法进行了广泛的实验,证明所提出的 CLT-Det 实现了有前景的和具有竞争力的性能。
更新日期:2022-09-06
down
wechat
bug