基于改进S3FD网络的人脸检测算法

Face detection algorithm based on improved S3FD network

为了解决人脸检测存在小目标人脸携带的特征信息少且相对较为模糊,导致检测难度较高的问题,采用将尺度不变人脸检测器(S3FD)网络与通道和空间注意力机制相结合的网络作为主干,在通道和空间上建立了特征之间的权重关系,强化特征提取能力,将原本S3FD所输出的特征图经扩大感受野后进行上采样,使得上一层特征图的输出包含了下一层特征图的特征。结果表明,widerface数据集的3个不同等级的验证集的平均精准率分别为95.0%,93.7%,86.4%,与原S3FD相比分别提高了1.3%,1.2%,0.5%。本文中提出的算法在人脸检测中具有较好的检测效果。

In face detection,the small target face carries less feature information and is relatively fuzzy,which leads to higher detection difficulty.In order to solve this problem,a novel algorithm was designed.The network that combines the single shot scale-invariant face detector(S3FD)network with the channel and the spatial attention mechanism was used as the backbone,and the channel and the spatial establish the weight relationship between the features,which strengthens the feature extraction ability.Then,the receptive field of the original S3FD output feature map was expanded and then up-sampled,so that the output of the feature map of the previous layer includes the features of the feature map of the next layer.Result:The average precision(AP)values of this algorithm on the three different levels of widerface verification datasets are 95.0%,93.7%,and 86.4%,respectively,which are increased by 1.3%,1.2%,and 0.5%compared with the original S3FD.The algorithm proposed in this paper has a better detection effect in face detection.