Authors: Botao Ren, Xue Yang, Yi Yu, Junwei Luo, Zhidong Deng
Abstract: Single point supervised oriented object detection has gained attention and
made initial progress within the community. Diverse from those approaches
relying on one-shot samples or powerful pretrained models (e.g. SAM), PointOBB
has shown promise due to its prior-free feature. In this paper, we propose
PointOBB-v2, a simpler, faster, and stronger method to generate pseudo rotated
boxes from points without relying on any other prior. Specifically, we first
generate a Class Probability Map (CPM) by training the network with non-uniform
positive and negative sampling. We show that the CPM is able to learn the
approximate object regions and their contours. Then, Principal Component
Analysis (PCA) is applied to accurately estimate the orientation and the
boundary of objects. By further incorporating a separation mechanism, we
resolve the confusion caused by the overlapping on the CPM, enabling its
operation in high-density scenarios. Extensive comparisons demonstrate that our
method achieves a training speed 15.58x faster and an accuracy improvement of
11.60%/25.15%/21.19% on the DOTA-v1.0/v1.5/v2.0 datasets compared to the
previous state-of-the-art, PointOBB. This significantly advances the cutting
edge of single point supervised oriented detection in the modular track.
Source: http://arxiv.org/abs/2410.08210v1
