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, UCASAOD, and ICDAR2015) demonstrate the effectiveness of the proposed method
DOTA1.0
| Backbone | mAP | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
|---|---|---|---|---|---|---|---|---|---|
| ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.6 | - | 2 | rotated_reppoints_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 66.45 | oc | 1x | 3.53 | 15.3 | - | 2 | sasm_reppoints_r50_fpn_1x_dota_oc | model | log |
@inproceedings{hou2022shape,
title={Shape-Adaptive Selection and Measurement for Oriented Object Detection},
author={Hou, Liping and Lu, Ke and Xue, Jian and Li, Yuqiu},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2022}
}