Title: BRAYOLOv7: an improved model based on attention mechanism and raspberry pi implementation for online education

Authors: Jiayi Wu; Yingqian Zhang; Lei Fu; Yunrong Luo; Hui Xie; Rongru Hua

Addresses: School of Education and Psychological Science, Sichuan University of Science and Engineering, Zigong 643000, China ' School of Civil Engineering, Sichuan University of Science and Engineering, Zigong 643000, China ' School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China ' School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China ' Technology Center, Sichuan Shengtuo Testing Technology Co., Ltd., Chengdu 610045, China ' Technology Center, Sichuan Shengtuo Testing Technology Co., Ltd., Chengdu 610045, China

Abstract: Traditional machine learning in the education industry is facing difficulties in accurately identifying students' emotions, impacting the personalised delivery of online education. To address this, we propose the development of an enhanced YOLOv7 model called BRAYOLOv7, which utilises the bi-level routing attention mechanism. Our approach includes adjusting the non-maximal suppression parameter to reduce accidental deletion and false detection of objects, employing random erasing and CutMix image augmentation techniques to enhance edge and contour information, integrating the improved convolutional block attention module (ICBAM) into the backbone structure, and replacing the sigmoid-weighted linear unit activation function with the funnel rectified linear unit activation function. Experimental results show the improved model achieving a mean average precision of 99% and notable improvements in precision. This study offers a technical solution for integrating emotion recognition into intelligent online education platforms to enhance evaluation and feedback for students.

Keywords: algorithm efficiency optimisation; computer vision; deep neural networks; educational technology; psychology; YOLOv7.

DOI: 10.1504/IJSNET.2024.141604

International Journal of Sensor Networks, 2024 Vol.46 No.1, pp.45 - 59

Received: 23 Feb 2024
Accepted: 04 Mar 2024
Published online: 26 Sep 2024 *

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