Abstract
In order to improve the teaching efficiency of English teachers in classroom teaching, the target detection algorithm in deep learning and the monitoring information from teachers are used, the target detection algorithm of deep learning Single Shot MultiBox Detector (SSD) is optimized, and the optimized Mobilenet-Single Shot MultiBox Detector (Mobilenet-SSD) is designed. After analyzing the Mobilenet-SSD algorithm, it is recognized that the algorithm has the shortcomings of large amount of basic network parameters and poor small target detection. The deficiencies are optimized in the following partThrough related experiments of student behaviour analysis, the average detection accuracy of the optimized algorithm reached 82.13%, and the detection speed reached 23.5 fps (frames per second). Through experiments, the algorithm has achieved 81.11% in detecting students' writing behaviour. This proves that the proposed algorithm has improved the accuracy of small target recognition without changing the operation speed of the traditional algorithm. The designed algorithm has more advantages in detection accuracy compared with previous detection algorithms. The optimized algorithm improves the detection efficiency of the algorithm, which is beneficial to provide modern technical support for English teachers to understand the learning status of students and has strong practical significance for improving the efficiency of English classroom teaching.
Highlights
At present, internationalization is in a stage of rapid development, and social enterprises have higher requirements for the English level of talent
Because college English teaching has the characteristics of the subject itself and needs to meet the overall requirements of current quality education. e teaching of English in colleges and universities strives for the comprehensive development of students, which makes the structure of college English teaching very complicated, and it is difficult to guarantee teaching efficiency
In the algorithm selected in this paper, the multitask convolutional neural (MTCNN) face detection model uses the image pyramid multiscale face detection method as the basis and uses its subnetwork to obtain the relevant features of the face in order to lay the foundation for correcting the direction of the face
Summary
Internationalization is in a stage of rapid development, and social enterprises have higher requirements for the English level of talent. E development of big data, using many monitoring resources in the classroom, combined with target detection in deep learning, provides research ideas for detecting student learning status and improving student teaching efficiency [1]. Erefore, scholars combine the time and context information of the video to perform target detection [2]. Long-Short Term Memory (LSTM), and Artificial Neural Network (ANN) to aggregate video time and context information to optimize the features of fuzzy frames, to make the detection accuracy better. The concept of key frames is introduced, the detection time is optimized, and optical flow-related technologies are used to give feature propagation. Recurrent Neural Networks (RNN) combined with lightweight and heavyweight feature extractors are interleaved and used to further improve the accuracy and speed of video target detection [5]. E structure is arranged as follows: Section 1 is the introduction, which introduces related research results in the detection field; Section 2 is the research method, which introduces the design process of the algorithm in detail; Section 3 is the experimental results, testing and analysing the performance of the designed algorithm; Section 4 is the conclusion, summarizing the research algorithm and explaining the future research direction
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