Cartoon face detection is a more challenging task than human face detection
due to many difficult scenarios is involved. Aiming at the characteristics of
cartoon faces, such as huge differences within the intra-faces, in this paper,
we propose an asymmetric cartoon face detector, named ACFD. Specifically, it
consists of the following modules: a novel backbone VoVNetV3 comprised of
several asymmetric one-shot aggregation modules (AOSA), asymmetric
bi-directional feature pyramid network (ABi-FPN), dynamic anchor match strategy
(DAM) and the corresponding margin binary classification loss (MBC). In
particular, to generate features with diverse receptive fields, multi-scale
pyramid features are extracted by VoVNetV3, and then fused and enhanced
simultaneously by ABi-FPN for handling the faces in some extreme poses and have
disparate aspect ratios. Besides, DAM is used to match enough high-quality
anchors for each face, and MBC is for the strong power of discrimination. With
the effectiveness of these modules, our ACFD achieves the 1st place on the
detection track of 2020 iCartoon Face Challenge under the constraints of model
size 200MB, inference time 50ms per image, and without any pretrained models.

本文提出了一种名为 ACFD 的不对称卡通脸检测器，它由多个不对称单次聚合模块 (AOSA) 组成的新颖骨干 VoVNetV3，不对称双向特征金字塔网络 (ABi-FPN)，动态锚点匹配策略 (DAM) 和相应的边际二元分类损失 (MBC) 等模块组成。使用这些模块，我们在不使用任何预训练模型且模型大小为 200MB、推断时间为 50ms 每张图像的条件下，取得了 2020 年 iCartoon Face 挑战赛检测赛道的第一名。