EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
Source abstract
Abstract
With the frequent occurrence of natural disasters and the acceleration of urbanization, it is necessary to carry out efficient evacuation, especially when earthquakes, fires, terrorist attacks, and other serious threats occur. However, due to factors such as small targets, complex posture, occlusion, and dense distribution, the current mainstream algorithms still have problems such as low precision and poor real-time performance in crowd person detection. Therefore, this paper proposes EAAnet, a crowd person detection algorithm. It is based on YOLOv5, with CBAM (Convolutional Block Attention Module) introduced into the backbone, BiFPN (Bidirectional Feature Pyramid Network) introduced into the neck, and combined with a loss function of CIoU_Loss to better predict the person number. The experimental results show that compared with other mainstream detection algorithms, EAAnet has achieved significant improvement in precision and real-time performance. The precision value of all categories was 78.6%, which was increased by 1.8. Among these, the categories of riders and partially visible person were increased by 4.6 and 0.8, respectively. At the same time, the parameter number of EAAnet is only 7.1M, with a calculation amount of 16.0G FLOPs. Therefore, it is proved that EAAnet has the ability of the efficient real-time detection of the crowd person and is feasible in the field of emergency management.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Video Surveillance and Tracking Methods · Anomaly Detection Techniques and Applications · Fire Detection and Safety Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
College of Electronic and Information Engineering, North China Institute of Science and Technology, Langfang 065201, China
College of Science, China Agricultural University, Beijing 100083, China
Funding
S&T Program of Hebei · horizontal projects of China Agricultural University
References (40)
Show source-provided references
1. Vehicle and Pedestrian Detection Algorithm Based on Improved YOLOv5
2. Lin, X., and Song, A. (2023, January 26). Research on improving pedestrian detection algorithm based on YOLOv5. Proceedings of the International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), Nanchang, China.10.1117/12.2682285 ↗
3. Research on lightweight pedestrian detection based on improved YOLOv510.21595/mme.2023.23719 ↗
4. Debris-flow monitoring and warning: Review and examples10.1016/j.earscirev.2019.102981 ↗
5. Recent trends in crowd analysis: A review
6. Faster R-CNN deep learning model for pedestrian detection from drone images10.1007/s42979-020-00125-y ↗
7. EnsembleNet: A hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models10.1007/s00521-022-07940-9 ↗
8. Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 16–18). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seoul, Republic of Korea.10.1109/CVPR42600.2020.00978 ↗
9. Liu, S., Chi, J., and Wu, C. (2021, January 22–24). FCOS-Lite: An Efficient Anchor-free Network for Real-time Object Detection. Proceedings of the 33rd Chinese Control and Decision Conference (CCDC), Kunming, China.10.1109/CCDC52312.2021.9601937 ↗
10. A two-stage image process for water level recognition via dual-attention CornerNet and CTransformer10.1007/s00371-022-02501-6 ↗
11. Personalized Sketch-Based Image Retrieval by Convolutional Neural Network and Deep Transfer Learning10.1109/ACCESS.2019.2894351 ↗
12. Zhang, H., Chang, H., Ma, B., Wang, N., and Chen, X. (2020, January 23–28). Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.10.1007/978-3-030-58555-6_16 ↗
13. Lin, T., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017, January 22–29). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.324 ↗
14. Zhang, Q., Liu, Y., Zhang, Y., Zong, M., and Zhu, J. (2023). Improved YOLOv3 integrating SENet and optimized GIoU loss for occluded pedestrian detection. Sensors, 23.10.3390/s23229089 ↗
15. Tang, F., Yang, F., and Tian, X. (2023). Long-Distance Person Detection Based on YOLOv7. Electronics, 12.10.3390/electronics12061502 ↗
16. Dai, K., Sui, X., Wang, L., Wu, Q., Chen, Q., and Gu, G. (2020, January 5–7). Research on multi-target detection method based on deep learning. Proceedings of the Seventh Symposium on Novel Photoelectronic Detection Technology and Application, Kunming, China.
17. Deep Learning-Based Object Detection Improvement for Fine-Grained Birds10.1109/ACCESS.2021.3076429 ↗
18. WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild10.1109/TMM.2019.2929005 ↗
19. 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.10.1007/978-3-030-01234-2_1 ↗
20. Tan, M., Pang, R., and Le, Q.V. (2020, January 13–19). EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
21. Taleb, N.O., BEN Maati, M.L., Nanne, M.F., Aboubekrine, A.M., and Chergui, A. (2021). Study of Haar-AdaBoost (VJ) and HOG-AdaBoost (PoseInv) Detectors for People Detection. Int. J. Adv. Comput. Sci. Appl., 12.10.14569/IJACSA.2021.0121266 ↗
22. A Trainable System for Object Detection10.1023/A:1008162616689 ↗
23. Wu, B., and Nevatia, R. (2006, January 17–22). Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), New York, NY, USA.
24. Maolin, L., and Shen, J. (2017). Fast Object Detection Method Based on Deformable Part Model (Dpm). (EP3183691A1), U.S. Patent.
25. Imagenet classification with deep convolutional neural networks10.1145/3065386 ↗
26. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
27. Visual object detection with deformable part models10.1145/2494532 ↗
28. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
29. Zhou, C., and Yuan, J. (2018, January 8–14). Bi-box regression for pedestrian detection and occlusion estimation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01246-5_9 ↗
30. A fast RetinaNet fusion framework for multi-spectral pedestrian detection10.1016/j.infrared.2019.103178 ↗
31. Peng, Q., Luo, W., Hong, G., Feng, M., Xia, Y., Yu, L., Hao, X., Wang, X., and Li, M. (2016, January 27–28). Pedestrian detection for transformer substation based on gaussian mixture model and YOLO. Proceedings of the 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.10.1109/IHMSC.2016.130 ↗
32. Hu, J., Shen, L., and Sun, G. (2018, January 18–23). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
33. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13–19). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
34. GeNET: A web application to explore and share Gene Co-expression Network Analysis data10.7717/peerj.3678 ↗
35. Adaptive Feature Pyramid Networks for Object Detection10.1109/ACCESS.2021.3100369 ↗
36. Qing, Y., Liu, W., Feng, L., and Gao, W. (2021). Improved YOLO Network for Free-Angle Remote Sensing Target Detection. Remote. Sens., 13.10.3390/rs13112171 ↗
37. Wang, H., Guo, E., Chen, F., and Chen, P. (2023). Depth Completion in Autonomous Driving: Adaptive Spatial Feature Fusion and Semi-Quantitative Visualization. Appl. Sci., 13.10.3390/app13179804 ↗
38. Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.10.1109/ICCV.2019.00972 ↗
39. Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20–25). RepVGG: Making VGG-style ConvNets Great Again. Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.01352 ↗
40. Focal loss for dense object detection10.1109/TPAMI.2018.2858826 ↗