Full convolution examination room target detection method based on cascade attention and point supervision mechanism

Abstract

The invention discloses a full convolution examination room target detection method based on cascade attention and a point supervision mechanism. The method comprises the steps of constructing a fullconvolution examination room target detection network model, training the full convolution examination room target detection network model and detecting a test set image. On the basis of an FCOS method, a cascade attention module is provided and inserted between a backbone network and a feature pyramid network, so that the feature pyramid network obtains remarkable features; point supervision branches are added to a full convolution detection head module, so that a detection model has the capability of distinguishing overlapped targets, the overlapped targets can be detected in an examinationroom monitoring scene with dense examinee seats, and the problem of inaccurate target detection bounding box regression in a dense scene is solved. The precision is 92.9%, the detection speed is 22.1frames per second, the precision and speed of an examination room target detection task are improved, and the method can be used for examination room detection.

Description

technical field [0001] The invention belongs to the cross-research technical field of educational science and image processing, and in particular relates to examinee target detection in a standardized examination room. Background technique [0002] Examination is the main way to assess the knowledge level and ability of learners. In order to regulate the order of the examination room, prevent candidates from cheating, and maintain the fairness and impartiality of the examination, our country currently mainly uses the combination of on-site examiner inspection and electronic video surveillance to conduct examinations. Due to the large amount of data and redundant information in the examination room monitoring video, the efficiency of the traditional examination room monitoring system is extremely low, and the monitoring personnel often suffer from visual fatigue due to high labor intensity. Abnormal behavior of candidates. Using advanced computer vision technology to serve ...

AI Technical Summary

Problems solved by technology

[0005] Considering the relatively dense distribution of examinee targets in the monitoring video of the real examination room and the large difference in imaging size due to the distance between the sitting position and the imaging device, it is difficult for the FCOS method to accurately detect them

In addition, in the FCOS method, if the marked real frames overlap, and the position (x, y) is mapped to the original image, multiple real frames will be obtained, then this position is considered to be a fuzzy sample, and the overlapping real frames may be lost in the training process. create intractable ambiguity

In the classroom monitoring scene, the targets are densely distributed, and most of the targets will occlude each other. It is difficult to determine the boundary of the detection frame only by using the multi-level prediction and Center-ness method in the FCOS method, which is easy to cause misjudgment.

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