Steel surface defect detection based on bidirectional cross-scale fusion deep network

Mahmood, A., Khan, S.A., Hussain, S., et al.: An adaptive image contrast enhancement technique for low-contrast images. IEEE Access 7, 161584–161593 (2019)

Article  MATH  Google Scholar 

He, Yu., Song, K., Meng, Q., Yan, Y.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69(4), 1493–1504 (2020)

Article  ADS  MATH  Google Scholar 

Nturambirwe, J., Opara, U.: Machine learning applica tions to non-destructive defect detection in horticultural products. Biosyst. Eng. 189, 60–83 (2020)

Article  Google Scholar 

Krummenacher, G., Ong, C.S., Koller, S., Kobayashi, S., Buhmann, J.M.: Wheel defect detection with machine learning. IEEE Trans. Intell. Transp. Syst. 19(4), 1176–1187 (2017)

Article  Google Scholar 

Saberironaghi, A., Ren, J., El-Gindy, M.: Defect detection methods for industrial products using deep learning techniques: a review. Algorithms 16, 95 (2023)

Article  MATH  Google Scholar 

Chen, Z., Zhang, J., Lai, Z., et al.: The devil is in the crack orientation: a new perspective for crack detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. Vancouver, Canada, pp. 6653–6663 (2023)

Huang, W., Zhu, G., Huang, Q., et al.: Defect screening on nuclear power plant concrete structures: a two-staged method based on contrastive representation learning. In: 2023 International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 691–697. IEEE, Tokyo, Japan (2023)

Fu, G., Sun, P., Zhu, W., et al.: A deep-learning-based approach for fast and robust steel surface defects classification. Opt. Lasers Eng. 121, 397–405 (2019)

Article  MATH  Google Scholar 

Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. Las Vegas, USA (2016)

Redmon, J.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475. Vancouver, Canada (2023)

Zhao, Y., Lv, W., Xu, S., et al.: DETRs beat YOLOs on real-time object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16965–16974. Seattle, USA (2024)

Carion, N., Massa, F., Synnaeve, G., et al.: End-to-end object detection with transformers. In: Computer Vision–ECCV 2020: 16th European Conference, pp. 213–229. Glasgow, UK (2020)

Zhu, X., Su, W., Lu, L., et al.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)

Meng, D., Chen, X., Fan, Z., et al.: Conditional DETR for fast training convergence. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3651–3660. Nashville, USA (2021)

Wang, Y., Zhang, X., Yang, T., et al.: Anchor DETR: query design for transformer-based detector. Proc AAAI Conf Artif Intell 36(3), 2567–2575 (2022)

MATH  Google Scholar 

Liu, S., Li, F., Zhang, H., et al.: DAB-DETR: dynamic anchor boxes are better queries for DETR. arXiv preprint arXiv:2201.12329 (2022)

Li, F., Zhang, H., Liu, S., et al.: DN-DETR: accelerate DETR training by introducing query denoising. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13619–13627. New Orleans, USA (2022)

Chen, Q., Chen, X., Wang, J., et al.: Group DETR: fast DETR training with group-wise one-to-many assignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6633–6642. Vancouver, Canada (2023)

Zhang, H., Li, F., Liu, S., et al.: DINO: DETR with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022)

He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. Las Vegas, USA (2016)

Yao, T., Li, Y., Pan, Y., et al.: HGNet: learning hierarchical geometry from points, edges, and surfaces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21846–21855. Vancouver, Canada (2023)

Wang, Z., Xie, K., Zhang, X., et al.: Small-object detection based on YOLO and dense block via image super-resolution. IEEE Access 9, 56416–56429 (2021)

Article  Google Scholar 

Wang, Y., Yang, L., Chen, H., et al.: Mushroom-YOLO: a deep learning algorithm for mushroom growth recognition based on improved YOLOv5 in agriculture 40. In: IEEE 20th International Conference on Industrial Informatics (INDIN), pp. 239–244. Perth, Australia (2022)

Huang, L., Chen, C., Yun, J., et al.: Multi-scale feature fusion convolutional neural network for indoor small target detection. Front. Neurorobot. 16, 881021 (2022)

Article  MATH  Google Scholar 

Liu, D., Cheng, F.: SRM-FPN: a small target detection method based on FPN optimized feature. In: Proceedings of International Computer Conference on Wavelet Active Media Technology, pp. 506–509. Chengdu, China (2021)

Liu, H., Duan, X., Chen, H., et al.: DBF-YOLO: UAV small targets detection based on shallow feature fusion. IEEJ Trans. Electr. Electr. 18, 605–612 (2023)

Article  MATH  Google Scholar 

Li, X., Zhang, Y., He, D.: Passenger flow detection in subway stations based on improved you only look once algorithm. Transp. Res. Rec. 2677(9), 397–409 (2023)

Article  MATH  Google Scholar 

Wang, S.: An augmentation small object detection method based on NAS-FPN. In: Proceedings of International Conference on Information Science and Control Engineering, pp. 213–218. Changsha, China (2020)

Chen, J., Kao, S., He, H., et al.: Run, don't walk: chasing higher FLOPS for faster neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12021–12031. Vancouver, Canada (2023)

Li, C., Li, L., Geng, Y., et al.: YOLOv6 v3.0: a full-scale reloading. arXiv preprint arXiv:2301.05586 (2023)

Sunkara, R., Luo, T.: No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 443–459. Grenoble, France (2022)

Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125. Honolulu, Hawaii (2017)

Liu, S., Qi, L., Qin, H., Shi, J., et al.: Path aggregation network for instance segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768. Salt Lake City, USA (2018)

Lv, X., Duan, F., Jiang, J., et al.: Deep metallic surface defect detection: the new benchmark and detection network. Sensors 20(6), 1562–1575 (2020)

Article  ADS  MATH  Google Scholar 

Xie, Z., Jin, L.: Non-stridden convolution and bidirectional cross-scale features fusion network for steel surface defect detection. In: International Conference on Intelligent Computing 2024, pp. 113–124. Tianjin, China (2024)

Comments (0)

No login
gif