LoCAR – Low-Cost Autonomous Robot for Object Detection with Voice Command and MobileNets

References

• Astua, C., R. Barber, J. Crespo, and A. Jardon. 2014. Object detection techniques applied on mobile robot semantic navigation. In Sensors, 6734–57. doi:10.3390/s140406734.
   Google Scholar
• Danneels, G. D., and K. R. Sampat. Color region based recognition of unidentified objects. US Patent 6,393,147. May 2002.
   Google Scholar
• De Winter, J., and J. Wagemans. 2004. Contour-based object identification and segmentation: Stimuli, norms and data, and software tools. Behavior Research Methods, Instruments, & Computers 36 (4):604–24. doi:10.3758/BF03206541.
   PubMedGoogle Scholar
• Debroy, S., N. Kumari, P. Singh, S. Kude, and P. Sheikh. International Journal of Computer Science and Mobile Computing Object Detection and Tracking Robot using Android, Arduino and Open CV. Tech. rep. 2016, pp. 187–91. www.ijcsmc.com.
   Google Scholar
• Everingham, M., L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2010. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision 88 (2):303–38. doi:10.1007/s11263-009-0275-4.
   Web of Science ®Google Scholar
• Fukushima, K., and S. Miyake. 1982. Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and cooperation in neural nets, IEEE Transactions on Systems, Man, and Cybernetics. SMC-13 (3): 826–834. doi:10.1109/TSMC.1983.6313076.
   Google Scholar
• Google. TextToSpeech Android Developers. 2018. https://developer.android.com/reference/android/speech/tts/TextToSpeech (visited on 10/ 30/2019)
   Google Scholar
• Howard, A. G., M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. In arXiv preprint arXiv:1704.04861.
   Google Scholar
• Kiatronics. 28BYJ-48 Datasheet. 2017. https://datasheetspdf.com/pdf/1006817/Kiatronics/28BYJ-48/1 (visited on 10/ 30/2019)
   Google Scholar
• Liang, H., Y. Gao, Y. Sun, and X. Sun. 2018. CEP: Calories estimation from food photos. International Journal of Computers and Applications 1–9. doi:10.1080/1206212X.2018.1486558.
   Google Scholar
• Lin, C. Y., and S. H. Lin. 2005. Artificial neural network based hole image interpretation techniques for integrated topology and shape optimization. Computer Methods in Applied Mechanics and Engineering 194 (36–38):3817–37. doi:10.1016/j.cma.2004.09.005.
   Web of Science ®Google Scholar
• Lin, T. Y., M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. “Microsoft coco: Common objects in context.” In: European conference on computer vision. Zurich, Switzerland: Springer. 2014, pp.740–55.
   Google Scholar
• Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. 2016. Ssd: Single shot multibox detector. In European conference on computer vision, 21–37. Springer, Cham. doi: 10.1007/978-3-319-46448-0_2
   Google Scholar
• Lucieer, A., A. Stein, and P. Fisher. 2005. Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty. International Journal of Remote Sensing 26 (14):2917–36. doi:10.1080/01431160500057723.
   Web of Science ®Google Scholar
• Ma, K., F. Huan, T. Liu, Z. Wang, and D. Tao. 2018. Deep blur mapping: Exploiting high-level semantics by deep neural networks. IEEE Transactions on Image Processing 27 (10):5155–66. doi:10.1109/TIP.2018.2847421.
   Web of Science ®Google Scholar
• Mao, J., and A. K. Jain. 1995. Artificial neural networks for feature extraction and multivariate data projection. IEEE Transactions on Neural Networks 6 (2):296–317. doi:10.1109/72.363467.
   PubMed Web of Science ®Google Scholar
• Osborne, M. J., and A. Rubinstein. A course in game theory. MIT press, 1994. Robotics, International Federation of. World Robotics Report 2016. 2016. https://ifr.org/ifr-press-releases/news/world-robotics-report-2016 (visited on 10/ 30/2019)
   Google Scholar
• Russell, S. J., and P. Norvig. 2016. Artificial intelligence: A modern approach. Boston, MA, USA: Pearson Education Limited.
   Google Scholar
• Sun, J., W. Cao, X. Zongben, and J. Ponce. “Learning a convolutional neural network for non-uniform motion blur removal.” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA. 2015, pp. 769–77.
   Google Scholar
• Szegedy, C., A. Toshev, and D. Erhan. 2013. Deep neural networks for object detection. In Advances in neural information processing systems, 2553–61.
   Google Scholar
• Tronco, M. L., A. M. Júnior, and A. J. V. Porto. 2003. Sistema de Reconhecimento de Imagens para Robô de Montagem: Implementação e Estudo de Caso. In VI Simpósio Brasileiro de Automac¸˜ao Inteligente. Bauru, setembro de. Universidade Estadual Paulista.
   Google Scholar