Algorithm for Counting the Number of Students in Class Based on Mask-RCNN Optimization

Abstract

In order to solve the problems of low efficiency, small scale, and data reliability in the traditional manual counting method, a method using mask region convolutional neural network (Mask R-CNN) to automatically calculate attendance was proposed. In order to extract deeper image information, the feature extraction network is designed as ResNet101, and feature map fusion is performed on multi-level feature maps. In order to make up for the lack of recognition of the objects whose body parts are occluded, the Mask R-CNN algorithm is used for the second recognition. The experimental results on the self-built classroom monitoring screenshot data set show that compared with the method of directly using the Mask R-CNN algorithm for recognition, the secondary recognition method can identify more targets and improve the accuracy of identifying the number of people.