Abstract
Surface defect detection is a common task in industry production. Generally, designer has to find out a suitable feature to separate defects in the image. The hand-designed feature always changes with different surface properties which lead to weak ability in other datasets. In this paper, we firstly present a general detecting method based on convolutional neural network (CNN) to overcome the common shortcoming. CNN is used to complete image patch classification. And features are automatically exacted in this part. Then, we build a voting mechanism to do a final classification and location. The good performances obtained in both arbitrary textured images and special structure images prove that our algorithm is better than traditional case-by-case detection one. Subsequently, we accelerate algorithm in order to achieve real-time requirements. Finally, multiple scale detection is proposed to get a more detailed locating boundary and a higher accuracy.
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References
Swain, M.J., Ballard, D.H.: Indexing via color histograms. In: Sood, A.K., Wechsler, H. (eds.) Active Perception and Robot Vision, pp. 261–273. Springer, Heidelberg (1992)
Conners, R.W., et al.: Identifying and locating surface defects in wood: part of an automated lumber processing system. IEEE Trans. Pattern Anal. Mach. Intell. 6, 573–583 (1983)
Vilnrotter, F.M., Nevatia, R., Price, K.E.: Structural analysis of natural textures. IEEE Trans. Pattern Anal. Mach. Intell. 1, 76–89 (1986)
Jolliffe, I.: Principal Component Analysis. John Wiley & Sons Ltd, Hoboken (2002)
Mandelbrot, B.B.: The Fractal Geometry of Nature, vol. 173. Macmillan, London (1983)
Mao, J., Jain, A.K.: Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recogn. 25(2), 173–188 (1992)
Comer, M.L., Delp, E.J.: Segmentation of textured images using a multiresolution Gaussian autoregressive model. IEEE Trans. Image Proc. 8(3), 408–420 (1999)
Le Cun, B.B., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems (1990)
Krizhevsky, A., Sutskever, I., Geoffrey, E., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
He, K., et al.: Deep residual learning for image recognition (2015). arXiv preprint arXiv:1512.03385
Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
DAGM 2007 Datasets. https://hci.iwr.uni-heidelberg.de/node/3616. Accessed 10 Apr 2017
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Wu, X., Cao, K., Gu, X. (2017). A Surface Defect Detection Based on Convolutional Neural Network. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_17
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DOI: https://doi.org/10.1007/978-3-319-68345-4_17
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