Abstract
Oriented object detection has received extensive attention in recent years, especially for the task of detecting targets in aerial imagery. Traditional detectors locate objects by horizontal bounding boxes (HBBs), which may cause inaccuracies when detecting objects with arbitrary oriented angles, dense distribution and a large aspect ratio. Oriented bounding boxes (OBBs), which add different rotation angles to the horizontal bounding boxes, can better deal with the above problems. New problems arise with the introduction of oriented bounding boxes for rotation detectors, such as an increase in the number of anchors and the sensitivity of the intersection over union (IoU) to changes of angle. To overcome these shortcomings while taking advantage of the oriented bounding boxes, we propose a novel rotation detector which redesigns the matching strategy between oriented anchors and ground truth boxes. The main idea of the new strategy is to decouple the rotating bounding box into a horizontal bounding box during matching, thereby reducing the instability of the angle to the matching process. Extensive experiments on public remote sensing datasets including DOTA, HRSC2016 and UCAS-AOD demonstrate that the proposed approach achieves state-of-the-art detection accuracy with higher efficiency.
Highlights
With the increasing number of applications based on convolutional neural networks (CNNs) in the field of computer vision, object detection algorithms have been developed rapidly
The proposed approach achieves state-of-the-art performance with high efficiency on three public remote sensing datasets annotated with oriented bounding boxes (OBBs): DOTA [19], HRSC2016 [20] and UCAS-AOD [21]
We evaluate the proposed detector on three public remote sensing datasets annotated with oriented bounding boxes, known as the DOTA [19], HRSC2016 [20] and UCAS-AOD [21] datasets
Summary
With the increasing number of applications based on convolutional neural networks (CNNs) in the field of computer vision, object detection algorithms have been developed rapidly. Related models typically use horizontal bounding boxes (HBBs) to locate targets. Most targets in remote sensing imageries are characterized by an arbitrary directionality, high aspect ratio and dense distribution; the models based on HBBs may cause serious overlap and noise. The rotating bounding box was devised to deal with these targets, with the advantages of capturing the target with better accuracy and introducing the least background noise. Oriented bounding boxes (OBBs) separate densely distributed targets perfectly and avoid the overlapping of the adjacent bounding boxes. For the detection of ships and vehicles, oriented detectors [7,8,9,10,11] based on rotating bounding boxes perform well
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