1,005
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Visual inspection of surface defects of extreme size based on an advanced FCOS

, , &
Article: 2122222 | Received 13 Jul 2022, Accepted 02 Sep 2022, Published online: 16 Sep 2022

References

  • Bae-Keun, K., W. Jong-Seob, and D.-J. Kang. 2015. Fast defect detection for various types of surfaces using random forest with vov features. International Journal of Precision Engineering and Manufacturing 16 (5):965–3142. doi:10.1007/s12541-015-0125-y.
  • Bauer, S., S. Köhler, K. Doll, et al. 2010. FPGA-GPU architecture for kernel SVM pedestrian detection. IEEE Computer Society Conference on Computer Vision Pattern Recognition Workshops (CVPRW): 61–68. doi: 10.1109/CVPRW.2010.5543772.
  • Chen, D.-S., and Z.-K. Liu. 2007. Generalized Haar-like features for fast face detection. International Conference on Machine learning Cyberne, Hong Kong: 2131–35. doi: 10.1109/ICMLC.2007.4370496.
  • Duje, M., P. Luka, S. Marko, B. Marko, and L. Sven. 2022. DefectDet: A deep learning architecture for detection of defects with extreme aspect ratios in ultrasonic images. Neurocomputing 473:107–15. doi:10.1016/j.neucom.2021.12.008.
  • Gan, G., and J. Cheng 2011. Pedestrian detection based on HOG-LBP feature. International Conference and Computing Intelligence Security, Hainan: 1184–87. doi:10.1109/CIS.2011.262.
  • Girshick, R., and Fast R-CNN2015. arXiv e-prints 1440–48. 10.48550/arXiv.1504.08083.
  • Girshick, R.-B., J. Donahue, T. Darrell, et al. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA: 580–87. doi: 10.1109/CVPR.2014.81.
  • Han, P., and J.-M. Liao. 2009. Face detection based on adaboost. International Conference Apperceiving Computing and Intelligence Analysis, Chengdu: 337–40. doi: 10.1109/ICACIA.2009.5361085.
  • He, K., X. Zhang, S. Ren, et al. 2016. Deep residual learning for image recognition. IEEE Conference on Computer Vision & Pattern Recognition, Las Vegas, NV, USA. doi: 10.1109/CVPR.2016.90.
  • Huang, L., Y. Yang, Y. Deng, et al. 2015. DenseBox: Unifying landmark localization with end to end object detection. Computer Science. doi:10.48550/arXiv.1509.04874.
  • Ioffe, S., and C. Szegedy. 2015. BaTch normalization: accelerating deep network training by reducing internal covariate shift. 32th International Conference on Machine Learning: 448–56. doi: 10.48550/arXiv:1502.03167.
  • Ji, W.-X., M. Du, W. Peng, et al. 2019. Research on gear appearance defect recognition based on improved faster R-CNN. Journal of System Simulation 31 (11):2198–205. doi:10.16182/j.issn1004731x.joss.19-0545.
  • Krizhevsky, A., I. Sutskever, and G.-E. Hinton. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM 60 (6):84–90. doi:10.1145/3065386.
  • Lin, T.-Y., P. Dollar, R. Girshick, et al. 2017. Feature pyramid networks for object detection. IEEE Conference on Computer Vision and Pattern Recognition. doi: 10.48550/arXiv.1612.03144.
  • Lin, T.-Y., P. Goyal, R. Girshick, et al. 2017. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis & Machine Intelligence 2980–88. doi:10.1109/TPAMI.2018.2858826.
  • Liu, W., D. Anguelov, D. Erhan, et al. 2016. SSD: Single shot multibox detector. European Conference on Computer Vision. doi: 10.1007/978-3-319-46448-0_2.
  • Liu, H.-B., and G.-W. Kang. 2005. Surface defects inspection of cold rolled strips based on neural network. Journal of Image and Graphics 10 (10):109–12. doi:10.11834/jig.2005010236.
  • Redmon, J., S. Divvala, R. Girshick, et al. 2016. You only look once: Unified, real-time object detection. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: 779–88. doi:10.1109/CVPR.2016.91.
  • Redmon, J., and A. Farhadi. 2017. YOLO9000: Better, Faster, stronger. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu: 6517–25. doi: 10.1109/CVPR.2017.690.
  • Redmon, J., and A. Farhadi. 2018. Yolov3: An incremental improvement. arXiv e-prints. doi:10.48550/arXiv.1804.02767.
  • Ren, S.-Q., K.-M. He, R. Girshick, et al. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6):91–99. doi:10.1109/TPAMI.2016.2577031.
  • Ren, F.-J., and S.-Y. Xue. 2020. Intention detection based on Siamese neural network with triplet loss. IEEE Access 8:82242–54. doi:10.1109/ACCESS.2020.2991484.
  • Rezatofighi, H., N. Tsoi, J.-Y. Gwak, et al. 2019. GeneralIzed intersection over union: a metric and a loss for bounding box regression. IEEE Conference on Computer Vision and Pattern Recognition: 658–66. doi: 10.1109/CVPR.2019.00075.
  • Shelhamer, E., J. Long, and T. Darrell. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (4):640–51. doi:10.1109/CVPR.2015.7298965.
  • Simonyan, K., and A. Zisserman. 2014. VeRy deep convolutional networks for large-scale image recognition. Computer Science. doi:10.48550/arXiv.1409.1556.
  • Theodoridis, S. 2015. Stochastic gradient descent. Machine Learning 161–231. doi:10.1016/B978-0-12-801522-3.00005-7.
  • Tian, Z., C. Shen, H. Chen, et al. 2019. FCOS: Fully convolutional one-stage object detection. IEEE/CVF International Conference on Computer Vision. doi: 10.48550/arXiv.1904.01355.
  • Tian, Z., C.-H. Shen, H. Chen, and T. He. 2020. FCOS: A simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence 1. doi:10.1109/TPAMI.2020.3032166.
  • Tianchi. 2018. https://tianchi.aliyun.com/competition/entrance/231682/information.
  • Wang, Z., Y. Jia, H. Huang, et al. 2008. Pedestrian detection using Boosted HOG features. Proceedings of the IEEE Conference Intelligence and Transport Systems, Beijing: 1155–60. doi:10.1109/ITSC.2008.4732553.
  • Wang, J., Q. Li, J. Gan, H. Yu, and X. Yang. 2020. Surface defect detection via entity sparsity pursuit with intrinsic priors. IEEE Transactions on Industrial Informatics 16 (1):141–50. doi:10.1109/TII.2019.2917522.
  • Wang, H. Y., J. Zhang, Y. Tian, et al. 2018. A simple guidance template-based defect detection method for strip steel surfaces. IEEE Transactions on Industrial Informatics. 15(5):2798–809. doi:10.1109/TII.2018.2887145.
  • Wu, Y.-X., and K.-M. He. 2018. Group Normalization. International Journal of Computer Vision. doi:10.1007/s11263-019-01198-w.
  • Yu, J., Y. Jiang, Z. Wang, et al. 2016. Unitbox: An advanced object detection network. ACM on Multimedia Conference: 516–20. doi: 10.1145/2964284.2967274.
  • Zhang, X., and D.-J. Huang. 2020. Defect detection on aluminum surfaces based on deep learning. Journal of East China Normal University (Natural Science) 06:105–14. doi:10.3969/j.issn.1000-5641.201921021.
  • Zhou, X.-Y., D.-Q. Wang, and P. Krahenbühl. 2019. Objects as Points. arXiv e-prints. doi:10.48550/arXiv.1904.07850.