Detecting the shuttlecock for a badminton robot: A YOLO based approach

作者：

Highlights：

• Tackle the challenging problem of detecting shuttlecock in real-time.

• Define a new loss function to better adapt to the task of detecting small objects.

• Modify state-of-the-art deep architecture to retain more semantic information.

• Results on real-world dataset show the effectiveness in both accuracy and speed.

摘要

•Tackle the challenging problem of detecting shuttlecock in real-time.•Define a new loss function to better adapt to the task of detecting small objects.•Modify state-of-the-art deep architecture to retain more semantic information.•Results on real-world dataset show the effectiveness in both accuracy and speed.

论文关键词：Deep learning,Object detection,YOLO,Badminton robot

论文评审过程：Received 18 September 2019, Revised 28 June 2020, Accepted 31 July 2020, Available online 7 August 2020, Version of Record 10 August 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113833