.png){kind=icon}
返回
Efficient Inductive Vision Transformer for Oriented Object Detection in Remote Sensing Imagery
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2023.3292418.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Object detection is a fundamental task in remote sensing image analysis and scene understanding. Previous remote sensing object detectors are typically based on convolutional neural networks (CNNs), whose performance is significantly limited by the intrinsic locality of convolution operations. The emergence of vision Transformers brings potential solutions to this problem, which has the capability to be a solid alternative to CNNs. However, three crucial obstacles hinder the application and performance of Transformers in the task of remote sensing object detection, that is: 1) high computational complexity, especially for high-resolution remote sensing images; 2) training and sample inefficiency caused by lack of inductive bias; and 3) difficulty in learning arbitrary orientation knowledge of geospatial objects. To address these issues, in this article, a novel efficient inductive vision Transformer framework is proposed for oriented object detection in remote sensing imagery. This framework follows the hierarchical feature pyramid structure and makes threefold contributions as follows: 1) spatial redundancy in remote sensing images is fully explored and an adaptive multigrained routing mechanism is proposed to facilitate token sparsity in Transformers, which can dramatically reduce the computational cost without comprising the accuracy. 2) A compact dual-path encoding architecture, where both global long-range dependencies and local semantic relations are jointly and complementarily captured, is proposed to enhance inductive bias in Transformers. 3) An angle tokenization technique is proposed to promote the encoding, embedding, and learning of direction knowledge for oriented objects in remote sensing scenarios. In this work, the above-mentioned three contributions are instantiated in an advanced Transformer-based object detector, namely, EIA-pyramid vision Transformer (PVT). Comprehensive experiments on two publicly available datasets have demonstrated its effectiveness and superiority for oriented object detection in remote sensing images.
Keyword:
Adaptive tokens
inductive biases
object detection
remote sensing imagery
vision Transformers
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
Multiscale Feature Enhancement Network for Salient Object Detection in Optical Remote Sensing Images多尺度特征增强网络在光学遥感图像显著目标检测中的应用
PREAI
分享收藏Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏分享收藏A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection面向任意对象检测的通用高斯热图标签分配
OAAI
OAAI分享收藏Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection用于超高分辨率遥感目标检测的分层鲁棒卷积神经网络
PREAI
分享收藏Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons当热带适应的人使用个人控制的空气运动时，热舒适性，感知的空气质量和认知表现.png)
Indoor Air
OAAI
Indoor AirIF0
OAAI分享收藏
查看更多内容