HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

Abstract

Object detection from remote sensing images is a key technology for Earth observation applications, which has important scientific research value. Ground objects in remote sensing images appear at arbitrary angles. However, object detection based on horizontal bounding boxes (HBBs) would cause mutual coverage among targets, while the ground objects were densely distributed or with a large aspect ratio. The oriented object detection methods could solve the problem by predicting the rotation angle. But the currently used methods were time-consuming in labeling and require complex loss functions of networks. Thus, this article combined HBB and oriented object detection to achieve rectangular object detection. First, an object detection network with HBBs is trained and the model predicts results in the original remote sensing image. Second, the rotation angles are derived from the line detection with linear Hough transform on the cropped region of targets obtained by the first step. Then, the original image is rotated according to the rotation angles and detects the object with HBBs again. Finally, new bounding boxes are mapped to the original image to get the detection results with oriented bounding boxes (OBBs). Experiments on public remote sensing datasets demonstrate that the proposed method is highly flexible and can be combined with any HBBs object detection network. The idea of original image rotation does not use the OBB labels to retrain the network, which reduces the workload of object annotations. In addition, the method can be used to automatically generate OBBs to label rectangular objects since the public datasets are commonly annotated by HBBs.

Keywords

• Deep learning
• line detection
• object detection
• oriented bounding boxes (OBBs)
• remote sensing