Call For Paper October 2026
Research Article | Open Access | Download PDF
Volume 13 | Issue 10 | Year 2026 | Article Id. IJECE-V13I10P106 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I10P106

TPU Accelerated Multi-Resolution and Occupancy based Performance Analysis of Embedded Real-Time Traffic Sign Detection on Raspberry Pi 5

Thotapalli Vinayakumar, Samineni Peddakrishna

Received Revised Accepted Published
08 Aug 2026 15 Sep 2026 24 Sep 2026 28 Oct 2026

Citation :

Thotapalli Vinayakumar, Samineni Peddakrishna, "TPU Accelerated Multi-Resolution and Occupancy based Performance Analysis of Embedded Real-Time Traffic Sign Detection on Raspberry Pi 5," International Journal of Electronics and Communication Engineering, vol. 13, no. 10, pp. 83-98, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I10P106

Abstract

Embedded traffic-sign detection must balance accuracy against the compute, memory and power limits of the target platform. This study evaluates YOLOv8n on TT100K at six independently trained input resolutions (640×640, 512×512, 480×480, 320×320, 224×224 and 160×160) and on three platform configurations: an NVIDIA GeForce RTX 3050 laptop GPU, a Raspberry Pi 5 CPU and a Raspberry Pi 5 with a Google Coral USB Edge TPU. Validation accuracy decreased monotonically from 0.812 mAP@50 at 640×640 to 0.088 at 160×160. By contrast, measured Edge TPU throughput remained between 87.70 and 89.33 FPS, so downsampling produced no meaningful throughput gain on the evaluated configuration while substantially reducing accuracy. Normalized sign occupancy was then related to recall for all 7,471 annotations in the test subset. Recall increased from 20.50% below 0.005% occupancy to 98.17% above 0.10%, with a transition region between approximately 0.02% and 0.05% observed for this dataset and model configuration. The combined analysis provides a platform-specific basis for selecting input resolution and camera geometry.

Keywords

Traffic sign detection, YOLOv8n, Edge AI, Raspberry Pi 5, Google Coral Edge TPU, Multi-Resolution analysis, Occupancy-based evaluation.

References

  1. Andreas Mogelmose, Mohan Manubhai Trivedi, and Thomas B. Moeslund, “Vision-based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp. 1484-1497, 2012.
    [CrossRef] [Google Scholar] [Publisher Link]
  2. Ayoub Ellahyani, Ilyas El Jaafari, and Said Charfi, “Traffic Sign Detection for Intelligent Transportation Systems: A Survey,” E3S Web of Conferences, vol. 229, pp. 1-10, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  3. Joseph Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779-788, 2016.
    [CrossRef] [Google Scholar] [Publisher Link]
  4. Junfan Wang et al., “Vehicle-Mounted Adaptive Traffic Sign Detector for Small-Sized Signs in Multiple Working Conditions,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 1, pp. 710-724, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  5. Junzhou Chen et al., “YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion,” IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 11, pp. 19995-20011, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  6. Gaihua Wang et al., “Traffic Sign Detection Method based on Improved YOLOv8,” Scientific Reports, vol. 15, pp. 1-13, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  7. Bin Ji et al., “Improved YOLOv8 for Small Traffic Sign Detection under Complex Environmental Conditions,” Franklin Open, vol. 8, pp. 1-9, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  8. Yuqi Li et al., “A Small-Target Traffic Sign Detection Algorithm based on Partial Conv and Atrous Spatial Pyramid,” IET Image Processing, vol. 18, no. 14, pp. 4639-4652, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  9. Yan Zhang et al., “A Lightweight Granular Perception Feature Pyramid Network with Context-Awareness for Small Traffic Sign Detection,” Expert Systems with Applications, vol. 317, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  10. Quanquan Tian, and Yang Zhang, “Synergising Heterogeneous Features and Guided Attention for Small Traffic Sign Detection in Complex Scenes,” Expert Systems, vol. 43, no. 6, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  11. Vidya Kamath et al., “Exploratory Data Preparation and Model Training Process for Raspberry Pi-Based Object Detection Model Deployments,” IEEE Access, vol. 12, pp. 45423-45441, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  12. David Minott, Salman Siddiqui, and Rami J. Haddad, “Benchmarking Edge AI Platforms: Performance Analysis of NVIDIA Jetson and Raspberry Pi 5 with Coral TPU,” SoutheastCon 2025, Concord, NC, USA, pp. 1384-1389, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  13. Arief Kurniawan et al., “Performance Comparison of Edge Devices for Deep Learning-based Object Detection and Classification,” 2025 International Seminar on Intelligent Technology and Its Applications (ISITIA), Surabaya, Indonesia, pp. 373-378, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  14. Daghash K. Alqahtani, Muhammad Aamir Cheema, and Adel N. Toosi, “Benchmarking Deep Learning Models for Object Detection on Edge Computing Devices,” 22nd International Conference Service-Oriented Computing, Tunis, Tunisia, pp. 142-150, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  15. Zhe Zhu et al., “Traffic-Sign Detection and Classification in the Wild,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 2110-2118, 2016.
    [CrossRef] [Google Scholar] [Publisher Link]
  16. Gülcan Yıldız, and Bekir Dizdaroğlu, “Traffic Sign Detection via Color and Shape-Based Approach,” 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey, pp. 1-5, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
  17. Radu Timofte, Karel Zimmermann, and Luc Van Gool, “Multi-View Traffic Sign Detection, Recognition, and 3D Localisation,” Machine Vision and Applications, vol. 25, no. 3, pp. 633-647, 2014.
    [CrossRef] [Google Scholar] [Publisher Link]
  18. Aashrith Vennelakanti et al., “Traffic Sign Detection and Recognition using a CNN Ensemble,” 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, pp. 1-4, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
  19. Jianming Zhang et al., “A Cascaded R-CNN with Multiscale Attention and Imbalanced Samples for Traffic Sign Detection,” IEEE Access, vol. 8, pp. 29742-29754, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  20. Zhang Hong, Liang Mingyin, and Wang Yufeng, “YOLO-BS: A Traffic Sign Detection Algorithm based on YOLOv8,” Scientific Reports, vol. 15, pp. 1-11, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  21. Bo Meng, and Weida Shi, “Small Traffic Sign Recognition Method Based on Improved YOLOv7,” Scientific Reports, vol. 15, no. 1, pp. 1-18, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. Xianzheng Liu et al., “A Feature Cascade and Recursive Fusion Architecture for Traffic Sign Detection in Vehicle Perception,” Scientific Reports, vol. 15, no. 1, pp. 1-13, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. Weiguo Pan et al., “ES-YOLO: Edge and Shape Fusion-Based YOLO for Traffic Sign Detection,” Computers, Materials and Continua, vol. 87, no. 1, pp. 1-19, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. Chen Xing, Haoran Sun, and Jiafu Yang, “A Lightweight Traffic Sign Detection Model based on Improved YOLOv8s for Edge Deployment in Autonomous Driving Systems Under Complex Environments,” World Electric Vehicle Journal, vol. 16, no. 8, pp. 1-18, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  25. Christine Dewi et al., “Robust Detection Method for Improving Small Traffic Sign Recognition based on Spatial Pyramid Pooling,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 7, pp. 8135-8152, 2021.
    [CrossRef] [Google Scholar] [Published Link]
  26. Chunfang Deng et al., “Extended Feature Pyramid Network for Small Object Detection,” IEEE Transactions on Multimedia, vol. 24, pp. 1968-1979, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  27. Omid Nejati Manzari, Amin Boudesh, and Shahriar B. Shokouhi, “Pyramid Transformer for Traffic Sign Detection,” 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of, pp. 112-116, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  28. Ji Qiu et al., “DP-YOLO: A Lightweight Traffic Sign Detection Model for Small Object Detection,” Digital Signal Processing, vol. 165, pp. 1-15, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
  29. Domen Tabernik, and Danijel Skočaj, “Deep Learning for Large-Scale Traffic-Sign Detection and Recognition,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1427-1440, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
  30. Jianfeng Han, Chao Wang, and Jinnan Du, “HMS-YOLO: Multi-Scale Traffic Sign Detection for Complex Scenarios,” Digital Signal Processing, vol. 183, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  31. Yili Xu, and Xuanxuan Xiao, “PST-YOLO: A Lightweight, Robust Approach for Traffic Sign Detection via Polynomial-based and Progressive Scale Fusions,” Digital Signal Processing, vol. 169, pp. 1-13, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
  32. Lisha Cui et al., “Context-Aware Block Net for Small Object Detection,” IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 2300-2313, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  33. Xiyang Dai et al., “Dynamic Head: Unifying Object Detection Heads with Attentions,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 7369-7378, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  34. Ricardo C. Camara de M. Santos, Mateus Coelho, and Ricardo Oliveira, “Real-Time Object Detection Performance Analysis using YOLOv7 on Edge Devices,” IEEE Latin America Transactions, vol. 22, no. 10, pp. 799-805, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  35. Petros Amanatidis et al., “Cooperative Task Execution for Object Detection in Edge Computing: An Internet of Things Application,” Applied Sciences, vol. 13, no. 8, pp. 1-19, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  36. Quang Nhat Nguyen Le, Mamen Thomas Chembakasseril, and Ronny Hartanto, “Benchmark Analysis of Deep Learning Algorithms for Edge-Based Robotic Applications,” Array, vol. 30, pp. 1-12, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]