With the advancement and maturity of computer vision technology, its application in the field of education has become a new trend. This paper presents a research study on the improvement of YOLOv7 for classroom student object detection, which holds significant implications for integrating computer vision technology into the educational domain. During the detection of classroom student targets using YOLOv7, challenges such as dense distribution, severe occlusion, and small target sizes were identified. These challenges led to issues like missed detections and false positives[1]. Addressing these issues, this paper introduces a repulsion loss function, building upon the original loss function of YOLOv7, to enhance the regression of bounding boxes. Training on a custom classroom student dataset revealed an improvement in the detection performance of YOLOv7 after incorporating the modified loss function. In comparison to the original model, the enhanced YOLOv7 achieved an average precision of 78.8%, representing a 0.8% improvement. The results indicate that the addition of the repulsion loss function enhances the accuracy of YOLOv7 in detecting student targets in real classroom environments.
Nowadays online porn video broadcasting and downloading is very popular. In view of the widespread phenomenon of Internet pornography, this paper proposed a new method of pornographic video detection based on connected areas. Firstly, decode the video into a serious of static images and detect skin color on the extracted key frames. If the area of skin color reaches a certain threshold, use the AdaBoost algorithm to detect the human face. Judge the connectivity of the human face and the large area of skin color to determine whether detect the sensitive area finally. The experimental results show that the method can effectively remove the non-pornographic videos contain human who wear less. This method can improve the efficiency and reduce the workload of detection.
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