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Improving Image Caption Performance with Linguistic Context Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Real Time Detection of Surface Defects with Inception-Based MobileNet-SSD Detection Network

Authors : Jianwen Zhou, Wenjing Zhao, Lei Guo, Xinying Xu, Gang Xie

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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Abstract

Effective surface defect detection are of great significance for the production of high quality products. Aiming at real-time detection of surface defect, we propose a reusable and high-efficiency Inception-based MobileNet-SSD method for surface defect inspection in industrial environment. First, convolutional layers for feature extraction used in SSD were replaced by depthwise separable convolutions utilized in MobileNet so that the speed of the network can be faster. Then, the layer in the base network as convolutional feature layer is constructed as Inception which can extract more rich features through multiple convolution combinations of different scales. Finally, predictions from multiple feature maps with different resolutions are combined by the network to naturally handle objects of various sizes. Experimental results on a surface defect dataset containing 2750 images of 5 classes we established confirm that our network has competitive accuracy and is much faster. For 300 × 300 input, ours network achieves 96.1% mAP on DAGM 2007 test at 73FPS on a NVIDIA GTX 1080Ti, outperforming a comparable state-of-the-art FCN model.

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Literature
1.
Han, J., Zhang, D., Hu, X., et al.: Background prior-based salient object detection via deep reconstruction residual. IEEE Trans. Circuits Syst. Video Technol. 25(8), 1309–1321 (2014)
2.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
3.
Hu, H., Liu, Y., Liu, M., et al.: Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm. Neurocomputing 181, 86–95 (2016)CrossRef
4.
Yongzhi, M., Biao, Y., Hongfeng, M.A., et al.: Adaptive segmentation algorithm for rail surface defects image. J. Beijing Univ. Technol. 43(10), 1472–1479 (2017)
5.
Yan, P., Zhang, X., Ding, Y.: Vision inspection of metal surface defects based on infrared imaging. Acta Optica Sin. 31, 0312004 (2011)CrossRef
6.
Xu, K., Wang, L., Wang, J.: Surface defect recognition of hot-rolled steel plates based on Tetrolet transform. J. Mech. Eng. 52, 13–19 (2016)CrossRef
7.
Zhu, Q., Ren, J., Barclay, D., et al.: Automatic animal detection from kinect sensed images for livestock monitoring and assessment. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 1154–1157. IEEE (2015)
8.
Yan, Y., Ren, J., Zhao, H., et al.: Cognitive fusion of thermal and visible imagery for effective detection and tracking of pedestrians in videos. Cogn. Comput. 10(1), 94–104 (2018)CrossRef
9.
Zheng, J., Liu, Y., Ren, J., et al.: Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidimension. Syst. Signal Process. 27(4), 989–1005 (2016)MathSciNetCrossRef
10.
Swain, M.J., Ballard, D.H.: Indexing via color histograms. In: Sood, A.K., Wechsler, H. (eds.) Active Perception and Robot Vision. NATO ASI Series (Series F: Computer and Systems Sciences), vol. 83, pp. 261–273. Springer, Heidelberg (1992)CrossRef
11.
Conners, R.W., Mcmillin, C.W., Lin, K., 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)CrossRef
12.
Jolliffe, I.: Principal Component Analysis. Springer, Heidelberg (2011)MATH
13.
14.
Wang, Z., Ren, J., Zhang, D., et al.: A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing 287, 68–83 (2018)CrossRef
15.
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing systems, pp. 91–99 (2015)
16.
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
17.
He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)CrossRef
18.
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
19.
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 (2016)
20.
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
21.
Ren, Q., Geng, J., Li, J.: Slighter faster R-CNN for real-time detection of steel strip surface defects. In: 2018 Chinese Automation Congress (CAC), pp. 2173–2178. IEEE (2018)
22.
Li, J., Su, Z., Geng, J., et al.: Real-time detection of steel strip surface defects based on improved YOLO detection network. IFAC-PapersOnLine 51(21), 76–81 (2018)CrossRef
23.
24.
Zhang, A., Sun, G., Ren, J., et al.: A dynamic neighborhood learning-based gravitational search algorithm. IEEE Trans. Cybern. 48(1), 436–447 (2016)CrossRef
25.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:​1409.​1556
26.
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
27.
Sifre, L., Mallat, S.: Rigid-motion scattering for image classification. Ph.D. thesis, pp. 1–3 (2014)
28.
Howard, A.G., Zhu, M., Chen, B., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). arXiv preprint arXiv:​1704.​04861
29.
30.
31.
Yu, Z., Wu, X., Gu, X.: Fully convolutional networks for surface defect inspection in industrial environment. In: Liu, M., Chen, H., Vincze, M. (eds.) Computer Vision Systems. ICVS 2017. LNCS, vol. 10528. Springer, Cham (2017)
Metadata
Title
Real Time Detection of Surface Defects with Inception-Based MobileNet-SSD Detection Network
Authors
Jianwen Zhou
Wenjing Zhao
Lei Guo
Xinying Xu
Gang Xie
Copyright Year
2020
Book
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-030-39430-1

Electronic ISBN: 978-3-030-39431-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39431-8_49

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