H2RBOX: HORIZONTAL BOX ANNOTATION IS ALL YOU NEED FOR ORIENTED OBJECT DETECTION

Abstract

Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the rising demand for oriented object detection. This paper proposes a simple yet effective oriented object detection approach called H2RBox merely using horizontal box annotation for weakly-supervised training, which closes the above gap and shows competitive performance even against those trained with rotated boxes. The cores of our method are weakly-and self-supervised learning, which predicts the angle of the object by learning the consistency of two different views. To our best knowledge, H2RBox is the first horizontal box annotation-based oriented object detector. Compared to an alternative i.e. horizontal box-supervised instance segmentation with our post adaption to oriented object detection, our approach is not susceptible to the prediction quality of mask and can perform more robustly in complex scenes containing a large number of dense objects and outliers. Experimental results show that H2RBox has significant performance and speed advantages over horizontal box-supervised instance segmentation methods, as well as lower memory requirements. While compared to rotated box-supervised oriented object detectors, our method shows very close performance and speed. The source code is available at PyTorch-based MMRotate and Jittor-based JDet.

1. INTRODUCTION

In addition to the relatively matured area of horizontal object detection (Liu et al., 2020) , oriented object detection has received extensive attention, especially for complex scenes, whereby fine-grained bounding box (e.g. rotated/quadrilateral bounding box) is needed, e.g. aerial images (Ding et al., 2019; Yang et al., 2019a) , scene text (Zhou et al., 2017) One attractive question arises that if one can achieve weakly supervised learning for oriented object detection by only using (the more readily available) HBox annotations than RBox ones. One poten-



* Correspondence author is Junchi Yan. The work was in part supported by National Key Research and Development Program of China (2020AAA0107600), National Natural Science Foundation of China (62222607), and Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102).1 The annotation cost (in price) of the RBox is about 36.5% ($86 vs. $63) higher than that of the HBox according to https://cloud.google.com/ai-platform/data-labeling/pricing.



, retail scenes (Pan et al., 2020) etc. Despite the increasing popularity of oriented object detection, many existing datasets are annotated with horizontal boxes (HBox) which may not be compatible (at least on the surface) for training an oriented detector. Hence labor-intensive re-annotation 1 have been performed on existing horizontalannotated datasets. For example, DIOR-R (Cheng et al., 2022) and SKU110K-R (Pan et al., 2020) are rotated box (RBox) annotations of the aerial image dataset DIOR (192K instances) (Li et al., 2020) and the retail scene SKU110K (1,733K instances) (Goldman et al., 2019), respectively.

