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DSS-YOLOv7: An Enhanced YOLOv7-Based Algorithm for Classroom Behavior Detection

Published: 02 December 2024 Publication History

Abstract

This paper addresses the challenges of detecting students' hand-raising behavior in classrooms, which include small target sizes, complex student features, and distracting classroom background information. We propose an improved YOLOv7-based algorithm, Deformable Simulated-SE Enhanced YOLOv7 (DSS-YOLOv7), specifically designed for classroom behavior detection. The proposed algorithm introduces several enhancements: it optimizes the Feature Pyramid Networks (FPN) structure by incorporating Deformable Convolutions (DCNv2), which effectively capture variations in students' behaviors, thereby improving detection accuracy and robustness. Additionally, the Squeeze-and-Excitation (SE) attention mechanism is introduced to enhance the model's focus on important features and improve recognition accuracy. The integration of the Simple Attention Module (SimAM) helps to reduce model complexity and minimize confusion caused by background information. Furthermore, data augmentation techniques are applied to the dataset, significantly enhancing the model's feature learning capabilities. Experimental results demonstrate that the DSS-YOLOv7 model achieves a 2.6% improvement in mean Average Precision ([email protected]), reaching 85%, compared to YOLOv7 on the SCB-Dataset. The DSS-YOLOv7 model exhibits superior performance in detecting classroom behaviors under complex conditions.

References

[1]
Yating Li, Xin Qi, Abdul Khader Jilani Saudagar, Abdul Malik Badshah, Khan Muhammad, and Shuai Liu. 2023. Student behavior recognition for interaction detection in the classroom environment. Image and Vision Computing 136 (2023), 104726.
[2]
Christopher CY Yang and Hiroaki Ogata. 2023. Personalized learning analytics intervention approach for enhancing student learning achievement and behavioral engagement in blended learning. Education and Information Technologies 28, 3 (2023), 2509–2528.
[3]
Qiwu Luo, Xiaoxin Fang, Li Liu, Chunhua Yang, and Yichuang Sun. 2020. Automated visual defect detection for flat steel surface: A survey. IEEE Transactions on Instrumentation and Measurement 69, 3 (2020), 626–644.
[4]
Wei Chen, Bin Zou, Chuanzhen Huang, Jinzhao Yang, Lei Li, Jikai Liu, and Xinfeng Wang. 2023. The defect detection of 3D-printed ceramic curved surface parts with low contrast based on deep learning. Ceramics International 49, 2 (2023), 2881–2893.
[5]
Feng-Ping An. 2019. Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model. Complexity 2019, 1 (2019), 5069026.
[6]
Dhuha Abdulhadi Abduljabbar, Siti Zaiton Mohd Hashim, and Roselina Sallehuddin. 2020. Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends. Telecommunication Systems 74 (2020), 225–252.
[7]
Dewei Zhao, Faming Shao, Li Yang, Xiannan Luo, Qiang Liu, Heng Zhang, and Zihan Zhang. 2023. Object Detection Based on an Improved YOLOv7 Model for Unmanned Aerial-Vehicle Patrol Tasks in Controlled Areas. Electronics 12, 23 (2023), 4887.
[8]
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. 2019. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019), 9308–9316.
[9]
Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), 7132–7141.
[10]
Yunmou Liu, Hui Du, Zhuogen Li, and Pu Chen. 2024. A new hybrid GPU-CPU sparse LDLT factorization algorithm with GPU and CPU factorizing concurrently. Journal of Computational Science 79 (2024), 102312.
[11]
K. Elangovan, British Ashok Sontakke, and C. S. Anoop. 2020. Evaluation of a digital converter for linear and nonlinear temperature sensors. In Proceedings of the 2020 IEEE Sensors Applications Symposium (SAS), 1–6.
[12]
Xue Yang, Junchi Yan, Wenlong Liao, Xiaokang Yang, Jin Tang, and Tao He. 2022. Scrdet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 2 (2022), 2384–2399.
[13]
Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, and Jieping Ye. 2023. Object detection in 20 years: A survey. Proceedings of the IEEE 111, 3 (2023), 257–276.
[14]
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, José Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie, and Laith Farhan. 2021. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data 8 (2021), 1–74.
[15]
Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, and Kurt Keutzer. 2018. Shift: A zero flop, zero parameter alternative to spatial convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), 9127–9135.
[16]
Lingxiao Yang, Ru-Yuan Zhang, Lida Li, and Xiaohua Xie. 2021. Simam: A simple, parameter-free attention module for convolutional neural networks. In Proceedings of the International Conference on Machine Learning (2021), 11863–11874.

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    MLMI '24: Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI)
    August 2024
    306 pages
    ISBN:9798400717833
    DOI:10.1145/3696271
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 02 December 2024

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    Author Tags

    1. DCNv2
    2. SE
    3. SimAM
    4. YOLO
    5. classroom behavior detection

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