Recently, tremendous strides have been made in generic object detection when used to detect faces, and there are still some remaining challenges. In this paper, a novel method is proposed named multilevel single stage network for face detection (MSNFD). Three breakthroughs are made in this research. Firstly, multilevel network is introduced into face detection to improve the efficiency of anchoring faces. Secondly, enhanced feature module is adopted to allow more feature information to be collected. Finally, two-stage weight loss function is employed to balance network of different levels. Experimental results on the WIDER FACE and FDDB datasets confirm that MSNFD has competitive accuracy to the mainstream methods, while keeping real-time performance.

中文翻译：

---

用于人脸检测的多级单级网络

近来，在用于检测人脸的通用对象检测方面已取得了长足的进步，仍然存在一些挑战。在本文中，提出了一种新的名为多级单级人脸检测网络（MSNFD）的方法。这项研究取得了三个突破。首先，将多级网络引入人脸检测以提高锚定人脸的效率。其次，采用增强的特征模块以允许收集更多的特征信息。最后，采用两阶段减肥功能来平衡不同层次的网络。WIDER FACE和FDDB数据集上的实验结果证实，MSNFD在保持实时性能的同时，与主流方法相比具有竞争优势。