  Acne detection is crucial for interpretative diagnosis and precise treatment
of skin disease. The arbitrary boundary and small size of acne lesions lead to
a significant number of poor-quality proposals in two-stage detection. In this
paper, we propose a novel head structure for Region Proposal Network to improve
the proposals' quality in two ways. At first, a Spatial Aware Double Head(SADH)
structure is proposed to disentangle the representation learning for
classification and localization from two different spatial perspectives. The
proposed SADH ensures a steeper classification confidence gradient and
suppresses the proposals having low intersection-over-union(IoU) with the
matched ground truth. Then, we propose a Normalized Wasserstein Distance
prediction branch to improve the correlation between the proposals'
classification scores and IoUs. In addition, to facilitate further research on
acne detection, we construct a new dataset named AcneSCU, with high-resolution
imageries, precise annotations, and fine-grained lesion categories. Extensive
experiments are conducted on both AcneSCU and the public dataset ACNE04, and
the results demonstrate the proposed method could improve the proposals'
quality, consistently outperforming state-of-the-art approaches. Code and the
collected dataset are available in
https://github.com/pingguokiller/acnedetection.
