Student behavior detection method based on target detection

A technology of target detection and detection method, which is applied in the fields of instrument, calculation, character and pattern recognition, etc., can solve the problems of difficult to achieve real-time detection, long training time, slow convergence speed, etc., to improve network learning effect and ensure semantic information. , improve the detection effect

Pending Publication Date: 2019-11-05
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

However, the above technologies all need to be calculated on high-resolution feature maps or pictures, which leads to the slow inference speed of such methods and it is difficult to achieve real-time detection; and simply fusing multi-s

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  • Student behavior detection method based on target detection
  • Student behavior detection method based on target detection
  • Student behavior detection method based on target detection

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Embodiment Construction

[0035] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0036] The present invention provides a student behavior detection method based on target detection, such as figure 1 shown, including the following steps:

[0037] S1. Establish a data set containing student behavior information, said student behavior including raising hands, standing and sleeping;

[0038] S2. Establish a student behavior detection model, which is an improved Faster R-CNN model based on the residual network ResNet-101;

[0039] S3. Based on the data set, the online difficult sample mining method is used to train the student behavior detection model;

[0040] S4. Use

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Abstract

The invention relates to a student behavior detection method based on target detection, and the method comprises the following steps: S1, building a data set comprising student behavior information which comprises hand raising, standing and sleeping; S2, establishing a student behavior detection model, wherein the model is an improved Faster R-CNN model based on a residual network ResNet-101; S3,training the student behavior detection model based on an online hard sample mining method; S4, detecting a to-be-detected video by using the trained student behavior detection model to obtain a student behavior result, and visualizing the student behavior result; wherein the residual network ResNet-101 adopts a multi-layer feature fusion strategy and the fifth convolution stage of the residual network ResNet-101 includes multiple branches with different receptive field sizes.. Compared with the prior art, the method has the advantages of high precision and the like.

Description

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Claims

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Application Information

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Owner SHANGHAI JIAO TONG UNIV
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