关键词: 深度学习, 小脸检测, 实时检测, 计算机视觉

Abstract: Tiny face detection in real-time scenes has a low detection rate and poor regression accuracy. This paper further integrates the lower-level feature maps for multi-scale prediction. According to the characteristics of face in real-time scene detection, predicted boxes of different scales are generated to better adapt to human face shape. In the prediction stage, a soft and hard nms algorithm based on Intersection of Union（IOU） discrimination is proposed to suppress the redundant prediction boxes. Two thresholds are set to divide the prediction frame generated by the network into three segments of low, medium and high, and different segments of the prediction boxes are treated differently to achieve accurate suppress. The optimal architecture of the paper can obtain 45 frame per second in real-time video detection and camera detection under two NVIDIA GTX 1080 graphics cards, and achieves an average accuracy of 82.6% on the Wider Face overall validation set.

Key words: deep learning, tiny face detection, real-time detection, computer vision