Abstract
The industrial robot training platform is close to the industrial field environment, which enables students to comprehensively learn and master robot application skills, among which machine vision is an important part of industrial robot. Machine vision can be applied to workpiece completion detection. Therefore, based on the YOLOX model, the activation function is improved by multi-scale detection so that the image feature information can be better extracted, so as to train the coupling tooling and distinguish four different tooling. The results show that the mAP value of the improved YOLOX algorithm is 100%, and the accuracy of the improved YOLOX algorithm is obviously improved compared with that before the improvement, indicating that the improved YOLOX algorithm has a good effect.
This paper is based on the national key research and development project (2019YFB1312602).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bochkovskiy, A., Wang, C.-Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Jocher, G., et al.: YOLOv5. https://github.com/ultralytics/yolov5 (2021)
Law, H., Deng, J.: CornerNet: detecting objects aspaired keypoints. In: ECCV (2018)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: CVPR (2017)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: CVPR Workshops (2020)
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: ICCV (2019)
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, S., Yu, D. (2022). Workpiece Detection of Robot Training Platform Based on YOLOX. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_62
Download citation
DOI: https://doi.org/10.1007/978-3-031-13841-6_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13840-9
Online ISBN: 978-3-031-13841-6
eBook Packages: Computer ScienceComputer Science (R0)