  Oriented object detection is a crucial task in computer vision. Current
top-down oriented detection methods usually directly detect entire objects, and
not only neglecting the authentic direction of targets, but also do not fully
utilise the key semantic information, which causes a decrease in detection
accuracy. In this study, we developed a single-stage rotating object detector
via two points with a solar corona heatmap (ROTP) to detect oriented objects.
The ROTP predicts parts of the object and then aggregates them to form a whole
image. Herein, we meticulously represent an object in a random direction using
the vertex, centre point with width, and height. Specifically, we regress two
heatmaps that characterise the relative location of each object, which enhances
the accuracy of locating objects and avoids deviations caused by angle
predictions. To rectify the central misjudgement of the Gaussian heatmap on
high-aspect ratio targets, we designed a solar corona heatmap generation method
to improve the perception difference between the central and non-central
samples. Additionally, we predicted the vertex relative to the direction of the
centre point to connect two key points that belong to the same goal.
Experiments on the HRSC 2016, UCASAOD, and DOTA datasets show that our ROTP
achieves the most advanced performance with a simpler modelling and less manual
intervention.
