Recently, anchor-based methods have achieved great progress in face detection. Once anchor design and anchor matching strategy determined, plenty of positive anchors will be sampled. However, faces with extreme aspect ratio always fail to be sampled according to standard anchor matching strategy. In fact, the max IoUs between anchors and extreme aspect ratio faces are still lower than fixed sampling threshold. In this paper, we firstly explore the factors that affect the max IoU of each face in theory. Then, anchor matching simulation is performed to evaluate the sampling range of face aspect ratio. Besides, we propose a Wide Aspect Ratio Matching (WARM) strategy to collect more representative positive anchors from ground-truth faces across a wide range of aspect ratio. Finally, we present a novel feature enhancement module, named Receptive Field Diversity (RFD) module, to provide diverse receptive field corresponding to different aspect ratios. Extensive experiments show that our method can help detectors better capture extreme aspect ratio faces and achieve promising detection performance on challenging face detection benchmarks, including WIDER FACE and FDDB datasets.
翻译:最近,基于锁定的方法在面对面检测方面取得了很大进展。 一旦锁定设计和锁定匹配策略确定, 将大量对正锚进行抽样。 但是, 极端侧比的面部总是无法按照标准锁定匹配策略进行抽样。 事实上, 锚和极端侧比面的最大IOU值仍然低于固定的取样阈值。 在本文中, 我们首先探索影响每个面部最大IOU的理论因素。 然后, 进行锁定匹配模拟, 以评价面部比例的抽样范围。 此外, 我们提出一个宽视比比战略, 以收集来自地铁面部更具代表性的正面锚值, 跨方位比例。 最后, 我们提出了一个新型的增强功能模块, 名为“ 感知场多样性” 模块, 以提供与不同方面比率相对的多种可接受字段。 广泛的实验表明, 我们的方法可以帮助检测出更好的极端面比面比例, 并在具有挑战性的脸检测基准( 包括 WIDER FACE 和 FDFDB 数据集) 上取得有希望的检测效果。