  Current state-of-the-art two-stage detectors generate oriented proposals
through time-consuming schemes. This diminishes the detectors' speed, thereby
becoming the computational bottleneck in advanced oriented object detection
systems. This work proposes an effective and simple oriented object detection
framework, termed Oriented R-CNN, which is a general two-stage oriented
detector with promising accuracy and efficiency. To be specific, in the first
stage, we propose an oriented Region Proposal Network (oriented RPN) that
directly generates high-quality oriented proposals in a nearly cost-free
manner. The second stage is oriented R-CNN head for refining oriented Regions
of Interest (oriented RoIs) and recognizing them. Without tricks, oriented
R-CNN with ResNet50 achieves state-of-the-art detection accuracy on two
commonly-used datasets for oriented object detection including DOTA (75.87%
mAP) and HRSC2016 (96.50% mAP), while having a speed of 15.1 FPS with the image
size of 1024$\times$1024 on a single RTX 2080Ti. We hope our work could inspire
rethinking the design of oriented detectors and serve as a baseline for
oriented object detection. Code is available at
https://github.com/jbwang1997/OBBDetection.

Computer Vision and Pattern Recognition