.png){kind=icon}
.png){kind=icon}
Return
An Automatic Defect Detection System for Synthetic Shuttlecocks Using Transformer Model
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3165224.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With an estimation of 220 million people playing badminton on a regular basis, it was particularly popular in Asia but has growing popularity in different regions of the world. The demands of the relevant products, such as shuttlecocks and rackets, are also increasing in the sports industry. Synthetic shuttlecock, produced to offer similar experience and feel as feather shuttlecocks to players, is a more economical alternative to feather shuttlecocks. In addition to maintaining high throughput production for synthetic shuttlecocks with cost reduction, a more substantial improvement in quality control is desired as well. Since the defect detection of synthetic shuttlecocks is a challenging task, it heavily relies on human visual inspection at present. The existing manual quality-inspection process is not only error-prone but also considerably less efficient. In this paper, we propose an intelligent system to overcome these difficulties and bridge the gap between research and practice. Two cylinder grippers are designed to automatically deliver the shuttlecocks, a camera is used for capturing images and an end-to-end objection detection approach based on the Transformer model is investigated to recognize defects. Empirical results show that the proposed system obtains encouraging performance with AP(50) value of 87.5% and outperforms other methods. Ablation studies demonstrate that our approach can considerably boost the detection performance of synthetic shuttlecocks. Moreover, the processing speed is much faster than human operators and suitable for industrial applications.
Keywords:
Feature extraction
Detectors
Inspection
Feathers
Transformers
Task analysis
Manuals
Synthetic shuttlecocks
defect detection
intelligent system
transformer model
cylinder gripper
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W