计算机科学
目标检测
人工智能
特征(语言学)
背景(考古学)
探测器
计算机视觉
特征提取
模式识别(心理学)
班级(哲学)
对象(语法)
古生物学
电信
哲学
语言学
生物
作者
Ying Yu,Xi Yang,Jie Li,Xinbo Gao
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-16
被引量:24
标识
DOI:10.1109/tgrs.2022.3177255
摘要
Object detection in aerial images, different from general object detection, faces with several challenges such as arbitrary-oriented objects and extremely imbalanced foreground-background distribution. Although some recent proposed aerial object detection methods achieve promising results, they are mainly anchor-based detectors which rely heavily on pre-defined anchor boxes and the final detection performance is sensitive to anchor-related hyper-parameters. In contrast, in this paper, we present an anchor-free detector with feature enhancement and soft label assignment (FSDet) which adopts a simpler design and achieves competitive performance. Specifically, to address the feature misalignment for detecting oriented objects, we propose an oriented feature refinement module to align the features with oriented objects. To alleviate the background issue, we design a class-aware context aggregation module to integrate the intra-class context information and suppress background context. Moreover, we propose a soft label assignment mechanism to measure the weight of training samples within the arbitrary-oriented objects, which can concentrate more on representative items with regard to their potential to detect oriented objects, achieving a more stable optimization during training. Extensive experiments on several datasets suggest that the proposed method is superior to the state-of-the-art methods and achieves a better trade-off between speed and accuracy.
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