Student Classroom Behavior Detection Based on Improved YOLOv7-tiny Algorithm

doi:10.3969/j.issn.1006-2475.2025.06.002

Abstract

Abstract:
Abstract: In response to the challenges of small and densely packed targets， as well as recognition performance affected by various occlusions and lighting interferences in student classroom behavior detection， this paper proposes an improved algorithm for student classroom behavior detection based on YOLOv7-tiny， termed YOLOv7-tiny-SCBD. Firstly， a convolutional block attention module is introduced to extract more feature information in both channel and spatial dimensions， improving the model's recognition performance. Secondly， by utilizing content-aware feature recombination and upsampling operators， the network's receptive field is enlarged， and upsampling is dynamically performed according to the input feature map information to obtain clearer and more realistic upsampling results. Finally， the Inner-MPDIoU loss function is employed to comprehensively and accurately evaluate the similarity between predicted boxes and ground truth boxes， thereby enhancing the model's detection accuracy. Experimental results demonstrate that the proposed algorithm achieves a mAP@0.5 of 81.6% on the SCB3-U dataset， which is a 3.1 percentage points improvement over the original YOLOv7-tiny algorithm. With 6.19 M parameters， 13.5 calculations， and a detection frame rate of 85.39 frames per second， can effectively detect student classroom behavior.

Key words: Key words: YOLOv7-tiny, student classroom behavior detection, attention module, receptive field, Inner-MPDIoU
CARAFE

CLC Number:

TP391

BAI Jia, GU Lin. Student Classroom Behavior Detection Based on Improved YOLOv7-tiny Algorithm[J]. Computer and Modernization, 2025, 0(06): 9-15.

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