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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 1: AAAI-22 Technical Tracks 1

Shape-Adaptive Selection and Measurement for Oriented Object Detection

February 1, 2023

Authors

Liping Hou

University of Chinese Academy of Sciences, Beijing 100049, China


Ke Lu

University of Chinese Academy of Sciences, Beijing 100049, China Peng Cheng Laboratory, Shenzhen 518055, China


Jian Xue

University of Chinese Academy of Sciences, Beijing 100049, China


Yuqiu Li

University of Chinese Academy of Sciences, Beijing 100049, China


Proceedings:

No. 1: AAAI-22 Technical Tracks 1

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision I

Downloads:

Download PDF

Abstract:

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.

DOI:

10.1609/aaai.v36i1.19975


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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