  Oriented object detection is a challenging task in aerial images since the
objects in aerial images are displayed in arbitrary directions and are
frequently densely packed. The mainstream detectors describe rotating objects
using a five-parament or eight-parament representations, which suffer from
representation ambiguity for orientated object definition. In this paper, we
propose a novel representation method based on area ratio of parallelogram,
called ARP. Specifically, ARP regresses the minimum bounding rectangle of the
oriented object and three area ratios. Three area ratios include the area ratio
of a directed object to the smallest circumscribed rectangle and two
parallelograms to the minimum circumscribed rectangle. It simplifies offset
learning and eliminates the issue of angular periodicity or label point
sequences for oriented objects. To further remedy the confusion issue of nearly
horizontal objects, the area ratio between the object and its minimal
circumscribed rectangle is employed to guide the selection of horizontal or
oriented detection for each object. Moreover, the rotated efficient
Intersection over Union (R-EIoU) loss with horizontal bounding box and three
area ratios are designed to optimize the bounding box regression for rotating
objects. Experimental results on remote sensing datasets, including HRSC2016,
DOTA, and UCAS-AOD, show that our method achieves superior detection
performance than many state-of-the-art approaches.
