Shape-Adaptive Selection and Measurement for Oriented Object Detection
Keywords:Computer Vision (CV), Machine Learning (ML)
AbstractThe 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.
How to Cite
Hou, L., Lu, K., Xue, J., & Li, Y. (2022). Shape-Adaptive Selection and Measurement for Oriented Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36(1), 923-932. https://doi.org/10.1609/aaai.v36i1.19975
AAAI Technical Track on Computer Vision I