面向移动平台人脸检测的FaceYoLo算法

针对移动平台上人脸检测实时性不强的问题，提出了一种基于深度学习的FaceYoLo实时人脸检测算法。首先，在YoLov3检测算法的基础上，加入快速消化卷积层（RDCL）缩小输入空间，然后加入多尺度卷积层（MSCL）丰富不同检测尺度的感受野，最后加入中心损失和致密化策略加强模型的泛化能力和鲁棒性。实验结果表明，在GPU上测试时，该算法较YoLov3算法在速度上提高至原来的8倍，每幅图像的处理速度可达0.002 8 s；精度提高了2.1个百分点；在Android平台上测试时，该算法较最好的MobileNet模型在检测速率上从5 frame/s提升到10 frame/s。通过实验结果可知，该算法能有效提高人脸检测在移动平台上的实时性能。

FaceYoLo algorithm for face detection on mobile platform

Concerning the problem of low real-time performance of face detection on mobile platform,a FaceYoLo real-time face detection algorithm based on deep learning was proposed. Firstly,based on the YoLov3 detection algorithm,the Rapidly Digested Convolutional Layers (RDCL) were added to reduce the input space size, then Multiple Scale Convolutional Layers(MSCL)were added to enrich the receptive fields of different detection scales,and finally the central loss and densification strategy were added to strengthen the generalization ability and robustness of the model. The experimental results show that,when tested on the GPU,the proposed algorithm improves the speed by nearly 8 times compared with the YoLov3 algorithm,has the processing time of each image reached 0. 002 8 s,and increases the accuracy by 2. 1 percentage points;when tested on the Android platform,the proposed algorithm has the detection rate increased from 5 frame/s to 10 frame/s compared with the best MobileNet model,demonstrating that the algorithm can effectively improve the real-time performance of face detection on mobile platform.