  Accurate acne detection plays a crucial role in acquiring precise diagnosis
and conducting proper therapy. However, the ambiguous boundaries and arbitrary
dimensions of acne lesions severely limit the performance of existing methods.
In this paper, we address these challenges via a novel Decoupled Sequential
Detection Head (DSDH), which can be easily adopted by mainstream two-stage
detectors. DSDH brings two simple but effective improvements to acne detection.
Firstly, the offset and scaling tasks are explicitly introduced, and their
incompatibility is settled by our task-decouple mechanism, which improves the
capability of predicting the location and size of acne lesions. Second, we
propose the task-sequence mechanism, and execute offset and scaling
sequentially to gain a more comprehensive insight into the dimensions of acne
lesions. In addition, we build a high-quality acne detection dataset named
ACNE-DET to verify the effectiveness of DSDH. Experiments on ACNE-DET and the
public benchmark ACNE04 show that our method outperforms the state-of-the-art
methods by significant margins. Our code and dataset are publicly available at
(temporarily anonymous).
