Abstract
Tyre brand, its size, model, age and condition monitoring are critical for many vehicle users. The detection and the recognition of plastic components defects result essential. Image classification has become one of the key applications in image processing and computer vision domain. It has been used in several fields such as medical area and intelligent transportation. Recently, results of deep neural networks (DNN) foreshadow the advent of reliable classifiers to perform such visual tasks. DNNs require learning of many parameters from raw images; hence, several images with class annotations are needed. These images are very expensive since pixel-level annotations are required. In this paper, we introduce a deep learning approach to detect and classify five classes of plastic components defects. A novel dataset of tyre images is collected and the images are manually labelled. The experiments are conducted on this dataset by comparing the performances of three DNNs such as UNet, FPN and LinkNet. Results yield high values of F1-score and show the effectiveness and the suitability of the proposed approach.
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References
Bhamare, H., Student, M., Khachane, P., Professor, A.: Quality inspection of tire using deep learning based computer vision. Int. J. Eng. Tech. Res. 8, 701 (2019)
Chang, C.Y., Srinivasan, K., Wang, W.C., Ganapathy, G.P., Vincent, D.R., Deepa, N.: Quality assessment of tire shearography images via ensemble hybrid faster region-based convnets. Electronics 9(1), 45 (2020)
Chaurasia, A., Culurciello, E.: Linknet: Exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4. IEEE (2017)
Cui, X., Liu, Y., Zhang, Y., Wang, C.: Tire defects classification with multi-contrast convolutional neural networks. Int. J. Pattern Recognit Artif Intell. 32(04), 1850011 (2018)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Guo, Q., Wei, Z.W.: Tire defect detection using image component decomposition. Res. J. Appl. Sci. Eng. Technol. 4(1), 41–44 (2012)
Guo, Q., Zhang, C., Liu, H., Zhang, X.: Defect detection in tire x-ray images using weighted texture dissimilarity. J. Sens. 2016 (2016)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull Soc Vaudoise Sci Nat 37, 547–579 (1901)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Tan, M., Le, Q.V.: Efficientnet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019)
Wang, R., Guo, Q., Lu, S., Zhang, C.: Tire defect detection using fully convolutional network. IEEE Access 7, 43502–43510 (2019)
Zhang, Y., Cui, X., Liu, Y., Yu, B.: Tire defects classification using convolution architecture for fast feature embedding. Int. J. Comput. Intell. Syst. 11(1), 1056–1066 (2018)
Zhang, Y., Li, T., Li, Q.: Defect detection for tire laser shearography image using curvelet transform based edge detector. Opt. Laser Technol. 47, 64–71 (2013)
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Mameli, M., Paolanti, M., Mancini, A., Frontoni, E., Zingaretti, P. (2022). Detection and Classification of Defects in Plastic Components Using a Deep Learning Approach. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-95892-3_53
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DOI: https://doi.org/10.1007/978-3-030-95892-3_53
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