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).
Abstract (translated)
准确的痘痘检测在获取精确诊断和进行适当的治疗中发挥着关键作用。然而,痘痘症状的含糊边界和任意维度严重限制了现有方法的性能。在本文中,我们通过一种新的分离式序列检测头(DSDH)来解决这些挑战,该头易于主流二步检测器采用。DSDH为痘痘检测带来了两个简单的但有效的改进。首先,offset和scale任务被明确引入,并且它们的不兼容性通过我们的任务分离机制得到解决,这提高了预测痘痘症状位置和大小的能力。其次,我们提出了任务序列机制,并Sequential地执行offset和scale任务以更全面地了解痘痘症状维度。此外,我们建立了一个高质量的痘痘检测数据集名为ACNE-DET,以验证DSDH的有效性。ACNE-DET和公开基准ACNE04的实验结果表明,我们的方法比最先进的方法表现出色。我们的代码和数据集已公开可用(暂时匿名)。
URL
https://arxiv.org/abs/2301.12219