  Recently, many arbitrary-oriented object detection (AOOD) methods have been
proposed and attracted widespread attention in many fields. However, most of
them are based on anchor-boxes or standard Gaussian heatmaps. Such label
assignment strategy may not only fail to reflect the shape and direction
characteristics of arbitrary-oriented objects, but also have high
parameter-tuning efforts. In this paper, a novel AOOD method called General
Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an
anchor-free object-adaptation label assignment (OLA) strategy is presented to
define the positive candidates based on two-dimensional (2-D) oriented Gaussian
heatmaps, which reflect the shape and direction features of arbitrary-oriented
objects. Based on OLA, an oriented-bounding-box (OBB) representation component
(ORC) is developed to indicate OBBs and adjust the Gaussian center prior
weights to fit the characteristics of different objects adaptively through
neural network learning. Moreover, a joint-optimization loss (JOL) with area
normalization and dynamic confidence weighting is designed to refine the
misalign optimal results of different subtasks. Extensive experiments on public
datasets demonstrate that the proposed GGHL improves the AOOD performance with
low parameter-tuning and time costs. Furthermore, it is generally applicable to
most AOOD methods to improve their performance including lightweight models on
embedded platforms.
