Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved SSD

Abstract

This paper takes the classroom images of more than 50 classrooms in a university for nearly 4 months in a semester as the research object. The LabelImg manual annotation method is used to construct a detection data set including four behavioral states: listening to class, taking notes, playing with mobile phones, and sleeping. In order to effectively improve the detection accuracy of the data annotation model, we used data enhancement techniques such as cropping, rotation, and shading transformation to expand the number of dataset. Based on this dataset, an improved SSD model based on deep learning target detection technology is adopted. ResNet module is used to solve the problem that VGG module has poor detection results of students’ behavior state in picture analysis, FPN module is added to build RF-SSD detection model to improve the efficiency of image recognition to solve the problem of the low efficiency of small target recognition in the back of class. The experimental results show that RF-SSD has a great improvement in feature extraction ability and small target recognition accuracy compared with native SSD in self-constructed dataset, and can provide technical support and new ideas and methods for teaching management in universities.