Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

Christine Dewi, Rung Ching Chen*, Hui Yu, Xiaoyi Jiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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An extraordinary challenge for real-world applications is traffic sign recognition, which plays a crucial role in driver guidance. Traffic signals are very difficult to detect using an extremely precise, real-time approach in practical autonomous driving scenes. This article reviews several object detection methods, including Yolo V3 and Densenet, in conjunction with spatial pyramid pooling (SPP). The SPP principle is employed to boost the Yolo V3 and Densenet backbone networks to extract the features. Moreover, we adopt spatial pyramid pooling to learn object features more completely. These models are measured and compared with key measurement parameters such as average accuracy (mAP), working area size, detection time, and billion floating-point number (BFLOPS). Based on the experimental results, Yolo V3 SPP outperforms state-of-the-art systems. Specifically, Yolo V3 SPP obtains 87.8% accuracy for small (S) target, 98.0% for medium (M) target, and 98.6% for large target groups in the BTSD dataset. Our results have shown that Yolo V3 SPP obtains the highest total BFLOPS (66.111), and mAP (99.28%). Consequently, SPP upgrades the achievement of all experimental models.

Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
Early online date19 Nov 2021
Publication statusEarly online - 19 Nov 2021


  • CNN
  • Densenet
  • object recognition and detection
  • spatial pyramid pooling
  • Yolo V3 SPP


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