Abstract: Due to the problems of illumination change, passenger congestion and large noise interference outside the station, the accuracy of face detection technology for subway passenger flow into station is low nowadays. In order to improve the accuracy of face detection, based on the original network structure of YOLO2 lightweight target detection algorithm Tiny YOLO2, this paper firstly compresses feature maps with different number of 1×1 convolution layers, and then adjusts the size of feature maps to a unified size for cascading to obtain high-dimensional feature maps. We reduce the number of convolution kernels in the last few layers of the network, replace the 3×3 convolution layer of original network with 1×1 convolutional layer to get a deeper and narrower face detection network. The improved network has been trained on the Wider Face dataset and the subway inbound passenger flow dataset to obtain the final face detection model. The trained face detection model is loaded to test 300 randomly selected images of passengers outside the station. The test results show that compared with the Tiny YOLO2 original face detection algorithm, the recall rate is increased by 4.2%, and the detection speed of single image is increased by 6.5%. At the same time, it is tested on the FDDB dataset which is widely used for face detection algorithm evaluation. When the number of false detections is 200, the accuracy of face detection is 5% higher than that of Tiny YOLO 2 and 2% higher than that of SSD. Moreover, this algorithm can achieve a good balance between detection speed and accuracy, and has better generalization.

Key words: face detection, passenger flow outside the subway station, Tiny YOLO2, convolutional neural network, Deep Tiny YOLO2