Segmentation Method of Magnetic Tile Surface Defects Based on Deep Learning
DOI:
https://doi.org/10.15837/ijccc.2022.2.4502Keywords:
YOLACT Network, Deep Learning, Target Detection, Defect SegmentationAbstract
Magnet tile is an essential part of various industrial motors, and its performance significantly affects the use of the motor. Various defects such as blowholes, break, cracks, fray, uneven, etc., may appear on the surface of the magnet tile. At present, most of these defects rely on manual visual inspection. To solve the problems of slow speed and low accuracy of segmentation of different defects on the magnetic tile surface, in this paper, we propose a segmentation method of the weighted YOLACT model. The proposed model uses the resnet101 network as the backbone, obtains multi-scale features through the weighted feature pyramid network, and performs two parallel subtasks simultaneously: generating a set of prototype masks and predicting the mask coefficients of each target. In the prediction mask coefficient branch, the residual structure and weights are introduced. Then, masks are generated by linearly combining the prototypes and the mask coefficients to complete the final target segmentation. The experimental results show that the proposed method achieves 43.44/53.44 mask and box mAP on the magnetic tile surface defect dataset, and the segmentation speed reaches 24.40 fps, achieving good segmentation results.
References
[2] Bolya, D.; Zhou, C., Xiao, F. (2020). YOLACT++: Better Real-time Instance Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 99, 1-1, 2020. https://doi.org/10.1109/TPAMI.2020.3014297
[3] Girshick, R. (2015). Fast R-CNN, 2015 IEEE International Conference on Computer Vision (ICCV), 1440-1448, 2015. https://doi.org/10.1109/ICCV.2015.169
[4] He, K.; Gkioxari, G.; P Dollár. (2017). Mask R-CNN, Proceedings of the IEEE international conference on computer vision, 2961-2969, 2017. https://doi.org/10.1109/ICCV.2017.322
[5] He, K.; Zhang, X.; Ren, S.; Sun, J. (2016). Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778, 2016. https://doi.org/10.1109/CVPR.2016.90
[6] He, K.; Zhang, X.; Ren, S. (2016). Identity Mappings in Deep Residual Networks, Springer, Cham, 630-645, 2016. https://doi.org/10.1007/978-3-319-46493-0_38
[7] Huang, Y.; Qiu, C.; Guo, Y.;Wang, X.;Yuan, K. (2018). Surface Defect Saliency of Magnetic Tile, 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 612- 617, 2018. https://doi.org/10.1109/COASE.2018.8560423
[8] Lee, Y.; Park, J.; (2020). CenterMask: Real-Time Anchor-Free Instance Segmentation, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13906-13915, 2020. https://doi.org/10.1109/CVPR42600.2020.01392
[9] Li, X. Q.; Jiang, H. H.; Yin, G. F. (2014). Detection of surface crack defects on ferrite magnetic tile, NDT and E International, 62, 6-13, 2014. https://doi.org/10.1016/j.ndteint.2013.10.006
[10] Lin, T. Y.; Dollar, P.; Girshick, R. (2017). Feature Pyramid Networks for Object Detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 936-944, 2017. https://doi.org/10.1109/CVPR.2017.106
[11] Liu, G. P.;Hu, H. X.;Hu, R. H. (2015). Magnetic tile surface defect extraction based on texture features, Modern Manufacturing Engineering, 07, 119-123, 2015.
[12] Liu, W.; Anguelov, D.; Erhan, D. (2016). SSD: Single Shot MultiBox Detector, Springer, Cham, 21-37, 2016. https://doi.org/10.1007/978-3-319-46448-0_2
[13] Liu, S.; Qi, L.; Qin, H.; Shi, J.;Jia, J. (2018). Path Aggregation Network for Instance Segmentation, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8759-8768, 2018. https://doi.org/10.1109/CVPR.2018.00913
[14] Ren, S.; He, K.; Girshick, R. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, 28, 91-99, 2015.
[15] Tan, M.; Pang, R.;Le, Q. V. (2020). EfficientDet: Scalable and Efficient Object Detection, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10778-10787, 2020. https://doi.org/10.1109/CVPR42600.2020.01079
[16] Wang, X.; Kong, T.; Shen, C. (2020). SOLO: Segmenting objects by locations, European Conference on Computer Vision. Springer, Cham, 649-665, 2020. https://doi.org/10.1007/978-3-030-58523-5_38
[17] Wang, T. R.; Wang, M. Q.; Zhang, J. S. (2021). Wheel defect segmentation technology based on Mask R-CNN, Foreign Electronic Measurement Technology, 40(02), 1-5, 2021.
[18] Wu, X. Y.(2020). Detection and Segmentation of Industrial CT Image Defects by Mask RCNN[ D], Lanzhou Jiaotong University, 2020.
[19] Yang, C. L.;Yin, M.;Jiang, H. H.(2017). Magnetic tile surface crack detection based on nonsubsampled Shearlet transform, Transactions of the Chinese Society of Agricultural Machinery, 48(03), 405-412, 2017.
[20] Zhao D.D.; Zhao H.S.; Guan R.C.; Yang C. (2021). Efficient Building Extraction for High Spatial Resolution Images Based on Dual Attention Network, International Journal of Computers Communications & Control, 16(4), 4245, 2021. https://doi.org/10.15837/ijccc.2021.4.4245
[21] Zoph, B.; Vasudevan, V.;Shlens, J.;Le, Q. V.(2018). Learning Transferable Architectures for Scalable Image Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8697-8710, 2018. https://doi.org/10.1109/CVPR.2018.00907
Additional Files
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
