Sufficient Sampling Method of Ranking Loss for Acne Detection

Acne detection is a challenging task due to its small size and blurred boundaries. Previous methods are mostly based on traditional object detection models, where the detected objects’ classification scores could not represent the localization regression quality well. The recent ranking loss somewhat alleviates this problem by generating a ranking score to correct the classification score. In this work, we investigate the factors influencing ranking loss and reveal that the key factors are the sampling method and the weights of sample pairs. Based on this observation, two effective sampling methods are proposed for ranking loss by combining the characteristics of acne data. Firstly, the previous sampling method was to sample all the bounding boxes (Bboxes) in the image. The low scores of Bboxes of some ground truths restrict them from participating in sampling as negative samples. So, the Ground-truth-aware (Gt-aware) Sampling is proposed to make more Bboxes participate in training sufficiently. Secondly, the background Bbox accounts for most of the Bbox in acne images, yet previous sampling methods did not use these Bboxes. Hence, we propose Background Sampling (BGS) to make these Bboxes participate in training effectively. Extensive experiments are conducted on the public dataset ACNE04 and AcneSCU, showing that the proposed method can consistently outperform state-of-the-art methods. The code is available at https://github.com/StevenGerrad/acneDet.