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Studying the real-time face expression state of teachers in class was important to build an objective classroom teaching evaluation system based on AI. However, the face-to-face communication in classroom conditions was a real-time process that operated on a millisecond time scale. Therefore, in order to quickly and accurately predict teachers’ facial expressions in real time, this paper proposed an improved YOLOv5 network, which introduced the attention mechanisms into the Backbone model of YOLOv5. In experiments, we investigated the effects of different attention mechanisms on YOLOv5 by adding different attention mechanisms after each CBS module in the CSP1_X structure of the Backbone part, respectively. At the same time, the attention mechanisms were incorporated at different locations of the Focus, CBS, and SPP modules of YOLOv5, respectively, to study the effects of the attention mechanism on different modules. The results showed that the network in which the coordinate attentions were incorporated after each CBS module in the CSP1_X structure obtained the detection time of 25 ms and the accuracy of 77.1% which increased by 3.5% compared with YOLOv5. It outperformed other networks, including Faster-RCNN, R-FCN, ResNext-101, DETR, Swin-Transformer, YOLOv3, and YOLOX. Finally, the real-time teachers’ facial expression recognition system was designed to detect and analyze the teachers’ facial expression distribution with time through camera and the teaching video.

The Kmeans clustering algorithm is widely used for the advantages of simplicity and efficient operation. However, the lack of clustering centers in the algorithm usually causes incorrect category of some discrete points. Therefore, in order to obtain more accurate clustering results when studying the factors affecting the professional growth of outstanding teachers, this paper proposes an improved algorithm of Kmeans combined with DBSCAN. Observing the clustering results of the influencing factors and calculating the evaluation standard values of the clustering results, it is found that the optimized DB-Kmeans algorithm has obvious improvements in the accuracy of the clustering results, and the clustering effect of the algorithm on edge points is more advantageous than the original algorithms according to the scatter diagram.

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