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Academic Journal of Computing & Information Science, 2023, 6(2); doi: 10.25236/AJCIS.2023.060211.

Improved YOLOv7-based algorithm for elevator passenger detection


Juesi Xiao

Corresponding Author:
Juesi Xiao

College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350108, China


The task of identifying the number of passengers in an elevator plays an important role in optimizing the elevator algorithm and ensuring the safety of elevator passengers. In order to enable the elevator passenger-carrying algorithm to be deployed in edge devices, a lightweight elevator passenger-carrying algorithm based on improved yolov7 is designed, which reduces the model size while ensuring high precision. The proposed algorithm adopts a more lightweight attention mechanism structure in the head of the network. And made the lift-person-detection dataset as the experimental dataset. The dataset has nearly 2,000 image samples, including 900 training set images and 600 test set images. Experiments show that the proposed model achieves 98.9% mAP recognition accuracy. Compared with the 71.21MB model size of the original yolov7 network, the model size of the model proposed in this paper is reduced to 63.24MB, and the volume is reduced by 11.13%.


YOLOv7 network; Attention mechanism; Elevator passenger detection

Cite This Paper

Juesi Xiao. Improved YOLOv7-based algorithm for elevator passenger detection. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 2: 85-93. https://doi.org/10.25236/AJCIS.2023.060211.


[1] CHEN Jiuhong, ZHANG Haiyu. Design of Classroom Number Counting System Based on Deep Learning [J]. Software Guide, 2019, 18(10):27-29+35.

[2] Zhang Lidan. Research on Detection Algorithm of Crowding Degree in Bus Carriage and Passenger Count in Platform [D]. Chongqing University.DOI:10.27670/d.cnki.gcqdu.2021.002625.

[3] Zhou Xun, Tao Qingchuan. Research on HOG-based lift headcount counting method[J]. Modern Computer (Professional Edition),2014(03):42-45.

[4] JIN Xiaolei1, FAN Minghui1, PAN Peng2 System of Counting for the Number of People in the Elevator Based on ARM [J]. China Academic Journal Electronic PublishingHouse, 2018, 33(04): 30-33+62. DOI:10.19557/j.cnki.1001-9944.2018.04.007.

[5] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. arXiv preprint arXiv:2207.02696, 2022.

[6] HU J, SHEN L, SAMUEL A, et al. Gather-excite: exploiting feature context in convolutional neural networks [EB/OL]. [2021-05-25].

[7] WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]∥Proc.of the European Conference on Computer Vision, 2018: 3-19.

[8] LIU F J, TIA H J. Dual attention network for scene segmentation[C]∥Proc.of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 3146-3154.

[9] HU J, SHEN L, SUN G, et al. Squeeze-and-excitation networks[C]∥Proc.of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.

[10] MNIH V, HEESS N, GRAVES A. Recurrent models of visual attention[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems.Cambridge: MIT Press, 2014, 2: 2204-2212.

[11] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[EB/OL].[2016-03-19].https://arxiv.org/pdf/1409.0473.pdf.

[12] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[EB/OL]. [2017-12-06]. https: //arxiv.org/pdf/1706.03762.pdf.

[13] REN Huan, WANG Xuguang. Review of attention mechanism [J]. Journal of Computer Applications, 2021, Vol. 41 (201): 1-6

[14] Liu Y, Shao Z, Hoffmann N. Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions[J]. arXiv preprint arXiv:2112.05561, 2021.

[15] Sanghyun Woo, Jongchan Park, Joon-Y oung Lee, and In So Kweon. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV), pages.3–19, 2018.

[16] Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6848–6856, 2018.

[17] Zhang Z, Zheng Y, Hong X, et al. A novel elevator group control algorithm based on binocular-cameras corridor passenger detection and tracking [J]. Multimedia Tools and Applications, 2015, 74(6):1761-1775.

[18] LIU R, LEHMAN J, MOLINO P, et al. An intriguing failing of convolutional neural networks and the coordconv solution. [EB/ OL]. https:/ / arxiv. org/ abs/1807. 03247:arXiv, (2018-12-03), [2021-09-13]

[19] WANG Y, ZHEN P B, HOU J H, et al. Convolutional neural networks with dynamic regularization [J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(5): 2299-2304

[20] LIU Z H, ZHANG Y D, CHEN Y Z, et al. Detection of algorithmically generated domain names using the recurrent convolutional neural network with spatial pyramid pooling [J]. Entropy, 2020, 22(9): 1-20

[21] YUN S, HAN D, OH J S, et al. CutMix: regularization strategy to train strong classifiers with localizable features [C]∥2019 IEEE/ CVF International Conference on Computer Vision (ICC-V), Piscataway, USA, 2019: 6022-6031

[22] Mikulovich V I. Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm [J]. Sensors, 2021, 22.