Cyclist Detection Using Tiny YOLO v2

  • Karattupalayam Chidambaram SaranyaEmail author
  • Arunkumar Thangavelu
  • Ashwin Chidambaram
  • Sharan Arumugam
  • Sushant Govindraj
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1057)


This paper seeks to evaluate the performance of the state of the art object classification algorithms for the purpose of cyclist detection using the Tsinghua–Daimler Cyclist Benchmark. This model focuses on detecting cyclists on the road for its use in development of autonomous road vehicles and advanced driver-assistance systems for hybrid vehicles. The Tiny YOLO v2 algorithm is used here and requires less computational resources and higher real-time performance than the YOLO method, which is extremely desirable for the convenience of such autonomous vehicles. The model has been trained using the training images in the mentioned benchmark and has been tested for the test images available for the same. The average IoU for all the truth objects is calculated and the precision-recall graph for different thresholds was plotted.


Tiny Yolo v2 Tsinghua–Daimler Cyclist Benchmark Cyclist detection IoU 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Karattupalayam Chidambaram Saranya
    • 1
    Email author
  • Arunkumar Thangavelu
    • 1
  • Ashwin Chidambaram
    • 1
  • Sharan Arumugam
    • 1
  • Sushant Govindraj
    • 1
  1. 1.Vellore Institute of TechnologyVelloreIndia

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