Shape-Adaptive Selection and Measurement for Oriented Object Detection

The development of detection methods for oriented object detection remains a challenging task. A considerable obstacle is the wide variation in the shape (e. g. , aspect ratio) of objects. Sample selection in general object detection has been widely studied as it plays a crucial role in the performance of the detection method and has achieved great progress. However, existing sample selection strategies still overlook some issues: (1) most of them ignore the object shape information; (2) they do not make a potential distinction between selected positive samples; and (3) some of them can only be applied to either anchor-free or anchor-based methods and cannot be used for both of them simultaneously. In this paper, we propose novel flexible shape-adaptive selection (SA-S) and shape-adaptive measurement (SA-M) strategies for oriented object detection, which comprise an SA-S strategy for sample selection and SA-M strategy for the quality estimation of positive samples. Specifically, the SA-S strategy dynamically selects samples according to the shape information and characteristics distribution of objects. The SA-M strategy measures the localization potential and adds quality information on the selected positive samples. The experimental results on both anchor-free and anchor-based baselines and four publicly available oriented datasets (DOTA, HRSC2016, UCAS- AOD, and ICDAR2015) demonstrate the effectiveness of the proposed method.

Authors

• Liping Hou
• Ke Lu University of Electronic Science and Technology of China
• Jian Xue
• Yuqiu Li

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