Abstract
In process of docking of automated apparatus there is a problem of determining of them relative position. This problem may be effectively solved with algorithms for relative position calculation, based on television picture formed by camera, installed on one apparatus and observing another one, or docking position. Apparatus position and orientation calculates using visual landmarks positions and information about 3D configuration of observing object and visual landmarks’ relative positions. Visual landmarks detection algorithm is the crucial part of such solution. Study of ability of application of object detection system based on deep convolutional neural network to task of visual landmark detection will be discussed in this article. As an example, detection of visual landmarks on space docking images will be discussed. Neural network based detection system learned using images of International Space Station received in process of docking of cargo spacecrafts will be represented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Stepanov, D., Bakhshiev, A., Gromoshinskii, D., Kirpan, N., Gundelakh, F.: Determination of the relative position of space vehicles by detection and tracking of natural visual features with the existing TV-cameras. Analysis of Images, Social networks and Texts, Four International Conference, AIST 2015, Yekaterinburg, Russia, 9–11 April 2015, Revised Selected papers. Communications in Computer and Information Science, vol. 542, pp. 431–442 (2015)
Bakhshiev, A.V., Korban, P.A., Kirpan, N.A.: Software package for determining the spatial orientation of objects by TV picture in the problem space docking. In: Robotics and Technical Cybernetics, Saint-Petersburg, Russia, RTC, vol. 1, pp. 71–75 (2013)
InterSpace. The channel is hosting a record of all space launches in the world. https://www.youtube.com/channel/UC9Fu5Cry8552v6z8WimbXvQ. Accessed 19 Apr 2017
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural Information Processing Systems (NIPS) (2015)
Girshick, R.: Faster R-CNN (Python implementation). https://github.com/rbgirshick/py-faster-rcnn
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the Devil in the Details: Delving Deep into Convolutional Nets. British Machine Vision Conference (2014)
Model Zoo – BVLC. affe Wiki – GitHub. https://github.com/BVLC/caffe/wiki/Model-Zoo
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Fomin, I., Gromoshinskii, D., Bakhshiev, A. (2018). Object Detection on Images in Docking Tasks Using Deep Neural Networks. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-66604-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-66604-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66603-7
Online ISBN: 978-3-319-66604-4
eBook Packages: EngineeringEngineering (R0)