Towards Accurate Acne Detection via Decoupled Sequential Detection Head

TL;DR

This paper introduces a novel Decoupled Sequential Detection Head (DSDH) for improved acne detection, explicitly modeling location and size prediction tasks to enhance accuracy, validated on a new dataset and public benchmarks.

Contribution

The paper proposes a new DSDH that decouples and sequences location and size tasks, improving acne detection accuracy over existing methods.

Findings

Outperforms state-of-the-art methods on ACNE-DET and ACNE04 datasets.

Introduces a new high-quality acne detection dataset ACNE-DET.

Demonstrates significant accuracy improvements with the proposed DSDH.

Abstract

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).