Abstract
This paper focuses on the reproducibility aspects of our ICPR2020 paper titled Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors. More specifically, we will describe our efforts in making the proposed framework reproducible, the dataset reproducible, and the experiments reproducible. In addition, we argue that reproducibility is a step toward adoptable research, which is something all researchers should strive for. To promote future research, the implementation of the paper is publicly made available at https://github.com/hitachi-rd-cv/weakly-sup-crackdet.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buslaev, A.V., Parinov, A., Khvedchenya, E., Iglovikov, V.I., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. ArXiv e-prints (2018)
Chambon, S., Moliard, J.M.: Automatic road pavement assessment with image processing: review and comparison. Int. J. Geophys. 2011 (2011)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Inoue, Y., Nagayoshi, H.: Deployment conscious automatic surface crack detection. In: WACV, pp. 686–694. IEEE (2019)
Inoue, Y., Nagayoshi, H.: Crack detection as a weakly-supervised problem: towards achieving less annotation-intensive crack detectors. In: International Conference on Pattern Recognition (ICPR) (2020)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Proc. Syst. 25, 1097–1105 (2012)
Liu, Y., Yao, J., Lu, X., Xie, R., Li, L.: DeepCrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing 338, 139–153 (2019)
Mashable: Google’s artificial intelligence chief says ‘we’re in an AI Spring’ (2016). https://mashable.com/2016/05/20/google-ai-spring/
Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.: Automatic road crack detection using random structured forests. IEEE TITS 17(12), 3434–3445 (2016)
Synced: NeurIPS 2019—the numbers (2019). https://syncedreview.com/2019/12/12/neurips-2019-the-numbers/
Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV, pp. 474–485. Academic Press Professional, Inc. (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Inoue, Y. (2021). Reproducibility Aspects of Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors. In: Kerautret, B., Colom, M., Krähenbühl, A., Lopresti, D., Monasse, P., Talbot, H. (eds) Reproducible Research in Pattern Recognition. RRPR 2021. Lecture Notes in Computer Science(), vol 12636. Springer, Cham. https://doi.org/10.1007/978-3-030-76423-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-76423-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76422-7
Online ISBN: 978-3-030-76423-4
eBook Packages: Computer ScienceComputer Science (R0)
