Abstract
Deep learning-based frameworks have been widely used in object recognition, perception and autonomous navigation tasks, showing outstanding feature extraction capabilities. Nevertheless, the effectiveness of such detectors usually depends on large amounts of training data. For specific object-recognition tasks, it is often difficult and time-consuming to gather enough valuable data [10]. Data Augmentation has been broadly adopted to overcome these difficulties, as it allows to increase the training data and introduce variation in qualitative elements like color, illumination, distortion and orientation. In this paper, we leverage on the object detection framework YOLOv2 [12] to evaluate the behavior of an obstacle detection system for an autonomous boat designed for the International RoboBoat Competition. We are focused on how the overall performance of a model changes with different augmentation techniques. Thus, we analyze the features that the network learns by using geometric and pixel-wise transformations to augment our data. Our instances of interest are buoys and sea markers, thus to generate training data comprising these classes, we simulated the aquatic surface of the boat and collected data from the COCO dataset [8]. Finally, we discuss that significant generalization is achieved in the learning process of our experiments using different augmentation techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fawzi, A., Samulowitz, H., Turaga, D., Frossard, P.: Adaptive data augmentation for image classification. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3688–3692, September 2016. https://doi.org/10.1109/ICIP.2016.7533048
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Robot. Res. 32, 1231–1237 (2013)
Girshick, R.B., Donahue, J., Darrell, T., Malik, J., Berkeley, U.C.: Rich feature hierarchies for accurate object detection and semantic segmentation. Technical report (2013)
Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 2672–2680. Curran Associates, Inc. (2014). http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
Jo, H., Na, Y., Song, J.: Data augmentation using synthesized images for object detection. In: 2017 17th International Conference on Control, Automation and Systems (ICCAS), pp. 1035–1038, October 2017. https://doi.org/10.23919/ICCAS.2017.8204369
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images, vol. 1, January 2009
Lemley, J., Bazrafkan, S., Corcoran, P.: Smart augmentation learning an optimal data augmentation strategy. IEEE Access 5, 5858–5869 (2017). https://doi.org/10.1109/ACCESS.2017.2696121
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Muoz-Bulnes, J., Fernandez, C., Parra, I., Fernndez-Llorca, D., Sotelo, M.A.: Deep fully convolutional networks with random data augmentation for enhanced generalization in road detection. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 366–371, October 2017. https://doi.org/10.1109/ITSC.2017.8317901
Pepik, B., Benenson, R., Ritschel, T., Schiele, B.: What is holding back convnets for detection? In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 517–528. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24947-6_43
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning, December 2017
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. arXiv preprint arXiv:1612.08242 (2016)
Russakovsky, O.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, September 2014
Xiang, Y., Mottaghi, R., Savarese, S.: Beyond PASCAL: a benchmark for 3D object detection in the wild. In: IEEE Winter Conference on Applications of Computer Vision, pp. 75–82, March 2014. https://doi.org/10.1109/WACV.2014.6836101
Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. arXiv preprint arXiv:1708.04896 (2017)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017)
Acknowledgements
We would like to thank Tecnológico de Monterrey, WritingLabs and TecLabs for providing the equipment used in our experiments and financial support in the production of this work. Additionally, we extend our gratitude to VANTEC, the student group from ITESM that invited us to participate in the International RoboBoat Competition.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Navarro, I., Herrera, A., Hernández, I., Garrido, L. (2018). Data Augmentation in Deep Learning-Based Obstacle Detection System for Autonomous Navigation on Aquatic Surfaces. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Computational Intelligence. MICAI 2018. Lecture Notes in Computer Science(), vol 11289. Springer, Cham. https://doi.org/10.1007/978-3-030-04497-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-04497-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04496-1
Online ISBN: 978-3-030-04497-8
eBook Packages: Computer ScienceComputer Science (R0)