Abstract
Classroom teaching quality evaluation system can enable the school’s functional departments to accurately assess the performance of the teaching staff and current teaching operations. As per the requirements for cultivating high-quality talents, planned teaching staff construction and teaching reforms need to be carried out to promote teachers’ appointments. Improving the system makes the appointment process more scientific by giving due attention to the individual characteristics of all types of teachers while hiring them for related jobs. The system motivates the love of teaching, high academic level, high teaching level, and competitive teaching. In recent years, the rapid development of artificial intelligence and deep learning caused many colleges and universities to put forward the target of campus digitization and education informatization. The state of the classroom is a critical reference factor throughout the teaching and learning process for evaluating students’ acceptance of the course and the quality of the teaching. However, at present, the analysis of the classroom status is mainly conducted manually, which distracts teachers and is also not much precise. Therefore, finding a method that can improve the efficiency of classroom status analysis has great research significance. This study uses the deep neural network method to read each class’s video recording and analyze it from the aspects of students’ behavior and attendance. The system can realize class behavior and eventually evaluate the course quality employed to motivate teachers to improve teaching and overall quality of education.
Highlights
MethodologyAccording to the network structure of the SSD algorithm, the detection of small targets is completed in the feature map at the shallow level
(2) Aiming at the neural network model, this paper proposes an improved SSD model by replacing the backbone network with an improved MobileNet network. e deep separable convolutional network reduces the network parameters, thereby increasing the calculation speed. e information in the deeper feature maps is merged in the shallow layers to improve the small target recognition rate accuracy
Evaluation Index. is paper evaluates the model considering the detection time of a single frame image against the mean Average Precision. mAP [35] is the average of all AP values, where AP is the average precision calculated by considering the area under the curve composed of precision and recall [36]
Summary
According to the network structure of the SSD algorithm, the detection of small targets is completed in the feature map at the shallow level. This level contains less feature information, so the detection effect is not good enough. According to the traditional SSD model design structure, the first 14 improved deep separable convolutional layers are intercepted from the improved MobileNet (300 × 300) network to replace VGG16 as the backbone network of the improved algorithm presented in this paper. According to the model structure, the size of the extracted six feature layers decreases step by step from shallow to deep, where the earlier stages contain less abstract information than the latter. Where β represents the decay rate, SdR represents the cumulative gradient variable, ρ represents the learning rate, α represents a constant, the prevention denominator is 0, and R represents the parameter
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