Modern convolutional neural networks (CNNs)-based face detectors have achieved tremendous strides due to large annotated datasets. However, misaligned results with high detection confidence but low localization accuracy restrict the further improvement of detection performance. In this paper, the authors first predict high confidence detection results on the training set itself. Surprisingly, a considerable part of them exist in the same misalignment problem. Then, the authors carefully examine these cases and point out that annotation misalignment is the main reason. Later, a comprehensive discussion is given for the replacement rationality between predicted and annotated bounding-boxes. Finally, the authors propose a novel Bounding-Box Deep Calibration (BDC) method to reasonably replace misaligned annotations with model predicted bounding-boxes and offer calibrated annotations for the training set. Extensive experiments on multiple detectors and two popular benchmark datasets show the effectiveness of BDC on improving models' precision and recall rate, without adding extra inference time and memory consumption. Our simple and effective method provides a general strategy for improving face detection, especially for light-weight detectors in real-time situations.
翻译:以现代神经网络(CNNs)为基础的现代神经神经网络(CNNs)面对面探测器由于大量附加说明的数据集而取得了巨大的进步。然而,由于检测信任度高,但本地化精度低,结果不匹配,限制了检测绩效的进一步改善。在本文中,作者首先预测了培训本身的高度信心检测结果。令人惊讶的是,其中相当一部分存在同样的不匹配问题。然后,作者仔细研究这些案例,指出批注不匹配是主要原因。后来,全面讨论了预测和附加说明的捆绑盒之间的替代合理性。最后,作者提出了一种新型的Bound-Box深吸附校准(BDC)方法,以合理取代与模型预测的捆绑盒的错误描述,并为培训集提供了经校准的说明。关于多个探测器和两个流行基准数据集的广泛实验表明BDC在改进模型的精确度和回忆率方面的有效性,但又不增加额外时间和记忆消耗量。我们简单有效的方法为改进面部检查提供了一般性战略,特别是实时轻度探测器提供了一般性的战略。