YOLOv11-ASV: Research on Classroom Behavior Recognition Method Based on YOLOv11
Source abstract
Abstract
(1) Background: With the continuous development of intelligent education, classroom behavior recognition has become increasingly important in teaching evaluation and learning analytics. In response to challenges such as occlusion, scale differences, and fine-grained behavior recognition in complex classroom environments, this paper proposes an improved YOLOv11-ASV detection framework; (2) Methods: This framework introduces the Adaptive Spatial Pyramid Network (ASPN) based on YOLOv11, enhancing contextual modeling capabilities through block-level channel partitioning and multi-scale feature fusion mechanisms. Additionally, VanillaNet is adopted as the backbone network to improve the global semantic feature representation; (3) Conclusions: Experimental results show that on our self-built classroom behavior dataset (ClassroomDatasets), YOLOv11-ASV achieves 81.5% mAP50 and 62.1% mAP50–95, improving by 1.6% and 2.9%, respectively, compared to the baseline model. Notably, performance shows significant improvement in recognizing behavior classes such as “reading” and “writing” which are often confused. The experimental results validate the effectiveness of the YOLOv11-ASV model in improving behavior recognition accuracy and robustness in complex classroom scenarios, providing reliable technical support for the practical application of smart classroom systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human Pose and Action Recognition · Advanced Technologies in Various Fields · Emotion and Mood Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201600, China
References (28)
Show source-provided references
1. Classroom behavior recognition using convolutional neural networks
2. Zhang, J., Liu, Y., and Li, H. (2020, January 6–10). Real-time classroom behavior detection based on YOLOv3. Proceedings of the International Conference on Artificial Intelligence in Education 2020, Ifrane, Morocco.10.1109/ICAIE50891.2020.00029 ↗
3. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
4. You only look once: Unified, real-time object detection
5. Bochkovskiy, A. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv.
6. Jocher, G. (2025, September 12). YOLOv5: Implementing YOLO in PyTorch. Available online: https://github.com/ultralytics/yolov5.
7. Ultralytics (2025, December 25). YOLOv8: YOLO’s Latest Model for Object Detection and Segmentation. Available online: https://docs.ultralytics.com/models/yolov8/.
8. Ultralytics (2025, December 25). YOLOv11: Advancing YOLOv8 for Complex Detection Tasks. Available online: https://docs.ultralytics.com/models/yolo11/.
9. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23–28). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision (ECCV), Virtual Event. Available online: https://arxiv.org/abs/2005.12872.10.1007/978-3-030-58452-8_13 ↗
10. Lin, T.-Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., and Dollar, D. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA. Available online: https://ieeexplore.ieee.org/document/8100175.10.1109/CVPR.2017.106 ↗
11. Ghiasi, G., Lin, T., and Tan, M. (2019, January 15–20). NAS-FPN: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA. Available online: https://arxiv.org/abs/1904.07392.
12. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany. Available online: https://arxiv.org/abs/1807.06521.10.1007/978-3-030-01234-2_1 ↗
13. Wang, Q., Luo, X., and Wu, M. (2020, January 14–19). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA. Available online: https://arxiv.org/abs/1910.03151.
14. Cai, J., Zhang, X., and Liu, Y. (2021, January 19–25). PKINet: A pyramid contextual information network for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual Event. Available online: https://arxiv.org/abs/2102.11088.
15. HCF-Net: Hybrid context fusion network for dense object detection
16. Duan, H., Zhao, Y., Chen, K., Lin, D., and Dai, B. (2022, January 18–24). Revisiting Skeleton-Based Action Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.00298 ↗
17. Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., and Zheng, N. (2020, January 14–19). Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00119 ↗
18. Automatic Detection of Mind Wandering from Video in the Lab and in the Classroom10.1109/TAFFC.2019.2908837 ↗
19. Liu, Z., Zhang, H., Chen, Z., Wang, Z., and Ouyang, W. (2020, January 13–19). Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00022 ↗
20. Recognition of Student Classroom Behaviors Based on Moving Target Detection10.18280/ts.380123 ↗
21. Student Behavior Recognition Based on Multitask Learning
22. Zhou, H., Jiang, F., Si, J., Xiong, L., and Lu, H. (2022). StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking. arXiv.10.1109/ICASSP49357.2023.10094982 ↗
23. Basketball Action Recognition Based on the Combination of YOLO and a Deep Fuzzy LSTM Network10.1007/s11227-023-05611-7 ↗
24. Jia, Q., and He, J. (2024). Student Behavior Recognition in Classroom Based on Deep Learning. Appl. Sci., 14.10.3390/app14177981 ↗
25. A lightweight semantic segmentation network based on self-attention mechanism and state space model for efficient urban scene segmentation
26. Li, W., Guo, H., Liu, X., Liang, K., Hu, J., Ma, Z., and Guo, J. (2024). Efficient face super-resolution via wavelet-based feature enhancement network. arXiv.10.1145/3664647.3681088 ↗
27. Ma, X., Dai, X., Bai, Y., Wang, Y., and Fu, Y. (2024, January 16–22). Rewrite the stars. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR52733.2024.00544 ↗
28. Yu, W., and Wang, X. (2025, January 11–15). MambaOut: Do we really need Mamba for vision?. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR52734.2025.00423 ↗