Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved Single Shot Multibox Detector

To enhance the effective use of teaching video information in digital campuses, provide timely and accurate assessment of students’ classroom learning status, and offer technical support for teaching management, this paper introduces an improved SSD (Single Shot MultiBox Detector) object detection algorithm for intelligent analysis of students’ real classroom behavior. This article examines classroom teaching videos from four months of a semester at a certain university, collecting over 10,446 1080p video frames to create a dataset for annotation and training. To address the poor detection performance of the SSD model when identifying four behavior states—listening, sleeping, playing, and writing—of students in combined classes due to object occlusion and complex classroom environments, this paper introduces ResNet residual modules and a dual attention mechanism into the VGG-16 (Visual Geometry Group-16) network. This enhancement improves the model’s ability to detect student states in video frames under conditions of object occlusion. An FPN (Feature Pyramid Network) fusion model is constructed to address the low recognition accuracy of the SSD model for small objects in the back rows, thereby improving image recognition accuracy. The experimental results show that the improved SSD model has improved feature extraction ability and small object recognition accuracy compared to the original SSD model, with mAP increasing from 51.62% to 63.75%. The research results of this project can provide real-time information reference for university teaching management, help teachers understand students’ learning status promptly, and adjust teaching methods on time to improve teaching quality and classroom effectiveness.