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Title · Authors · Abstract · Keywords · DOI · References

Mobile Network Coverage Image Classification Method Based on Improved YOLOv8-cls Algorithm

TANG Tianbiao, WANG Shubo

(Chongqing Branch of China Unicom, Chongqing 401121, China)

Section：Artificial Intelligence · Vol. 2025, Issue 10 · Pages：50-57 · 2096-4706（2025）10-0050-08 · CLC number：TP391.4；TN929.5 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.10.010

Abstract

The mobile network coverage problem is the research hotspot in the field of communication. The traditional MR.TadvRsrp image classification and diagnosis relies on the skills and experience of engineers, which is cumbersome and has poor real-time performance. In order to solve this problem, this paper proposes an intelligent classification method based on improved YOLOv8-cls algorithm. This method uses YOLOv8n-cls network as the feature extraction network, integrates the mixed attention module (CBAM) in the backbone network to improve the feature extraction ability, and adds the Spatial Pyramid Pooling-Fast (SPP-Fast) module to enhance the multi-scale feature aggregation effect. By designing a multi-level receptive field fusion structure (MF), the problem of insufficient feature fusion caused by the lack of neck structure (Neck) in the YOLOv8n-cls network is compensated. The improved algorithm significantly improves the accuracy and processing speed of image classification. The experimental results show that the classification and diagnosis accuracy of this method for 4G/5G network coverage slice image from specific areas reaches 95.7%, and the processing speed (FPS) reaches 110 frames/second. It exhibits high accuracy and strong real-time performance, offering efficient evaluation support for network planning and operational optimization.

Keywords

mobile network coverage; image classification; multi-scale fusion; YOLOv8-cls

References

  1. [1] KRIZHEVSKY A，SUTSKEVER I，HINTON G E. ImageNet Classification with Deep Convolutional Neural Networks [J].Communications of the ACM，2017，60（6）：84-90.
  2. [2] SZEGEDY C，LIU W，JIA Y Q，et al. Going Deeper with Convolutions [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Boston：IEEE，2015：1-9.
  3. [3] HE K M，ZHANG X Y，REN S Q AND SUN J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification [C]//2015 IEEE International Conference on Computer Vision (ICCV).Santiago：IEEE，2015：1026-1034.
  4. [4] REDMON J，DIVVALA S，GIRSHICK R，et al. You Only Look Once: Unified, Real-Time Object Detection [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Las Vegas：IEEE，2016：779-788.
  5. [5] REDMON J，FARHADI A. YOLO9000: Better, Faster, Stronger [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu：IEEE，2017：6517-6525.
  6. [6] REDMON J，FARHADI A. YOLOv3: An Incremental Improvement [J/OL].arXiv:1804.02767 [cs.CV].[2024-11-03].https://arxiv.org/abs/1804.02767.
  7. [7] LU Y，QIU Z B，LIAO C B，et al. A GIS Partial Discharge Defect Identification Method Based on YOLOv5 [J/OL].Applied Sciences，2022，12（16）：8360[2024-10-27].https://doi.org/10.3390/app12168360.
  8. [8] ZENG J Y，ZHONG H. YOLOv8-PD: An Improved Road Damage Detection Algorithm Based on YOLOv8n Model [J/OL].Scientific Reports，2024，14（1）：12052[2024-10-06].https://doi.org/10.1038/s41598-024-62933-z.
  9. [9] ZHANG S F，CHIC，YA0 Y Q，et al. Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle：IEEE，2020：9759-9768.
  10. [10] LI C Y，LI L L，JLANG H L，et al. YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications [J/OL].arXiv:2209.02976 [cs.CV].[2024-11-05].https://arxiv.org/abs/2209.02976.
  11. [11] LI X，LYU C Q，WANG W H，et al. Generalized Focal Loss: Towards Efficient Representation Learning for Dense Object Detection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2022，45（3）：3139-3153.
  12. [12] TIAN Z，SHEN C H，CHEN H，et al. FCOS: Fully Convolutional One-Stage Object Detection [C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV).Seoul：IEEE，2019：9626-9635.
  13. [13] WOO S ，PARK J，LEE J-Y，et al. CBAM: Convolutional Block Attention Module [C]//Computer Vision-ECCV 2018: 15th European Conference, Munich, Germany.Munich：Springer-Verlag，2018：3-19.
  14. [14] Fu Haochen, Bai Tangbo, Xu Guiyang, et al. A crack detection algorithm for track slabs based on improved YOLOv8 [J]. Journal of Beijing Jiaotong University, 2024, 48 (6): 133-143
  15. [15] HE K M，ZHANG X Y，REN S Q. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition [J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，37（9）：1904-1916.
  16. [16] TANG H Y，LIANG S，YAO D，et al. A Visual Defect Detection for Optics Lens Based on the YOLOv5 -C3CA-SPPF Network Model [J].Optics Express，2023，31（2）：2628-2643.
  17. [17] LIU S，QI L，QIN H F，et al. Path Aggregation Network for Instance Segmentation [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City：IEEE，2018：8759-8768.
  18. [18] LIN T-Y，DOLLÁR P，GIRSHICK R，et al. Feature Pyramid Networks for Object Detection [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu：IEEE，2017：2117-2125.
  19. [19] TAN M X，PANG R M，LE Q V. EfficientDet: Scalable and Efficient Object Detection [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattl：IEEE，2020：10778-10787.
  20. [20] WANG Q L，WU B G，ZHU P F，et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle：IEEE，2020：11531-11539.
  21. [21] HE K M，ZHANG X Y，REN S Q，et al. Deep Residual Learning for Image Recognition [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Los Alamitos：IEEE，2016：770-778.
  22. [22] ZHANG Q L，YANG Y B. SA-Net: Shuffle Attention for Deep Convolutional Neural Networks [J/OL].arXiv:2102.00240 [cs.CV].[2024-10-07].https://arxiv.org/abs/2102.00240.
  23. [23] GUAN B X，ZHAO Y H，LI Y，An Ant Colony Optimization Based on Information Entropy for Constraint Satisfaction Problems [J/OL].Entropy，2019，21（8）：766[2024-09-28].https://doi.org/10.3390/e21080766.

About the authors

Tang Tianbiao (1987-), male, Han nationality, from Bazhong, Sichuan, engineer, master's student, research direction: machine learning and mobile network optimization; Wang Shubo (1993-), male, Han ethnicity, from Urumqi, Xinjiang, engineer, master's student, research direction: artificial intelligence and machine learning.