Abstract
Object recognition technology has matured to a point at which exciting applications have become possible. Indeed, industry has created a variety of computer vision products and services from the traditional area of machine inspection to more recent applications such as object detection, video surveillance, or face recognition. This paper is about achieving the goal of object recognition through advanced techniques like deep learning on handy devices like smartphones and tablets. Deep learning algorithms (Convolutional Neural Networks (CNN)) are used for the primary aim of object recognition. Images are clicked through the camera of the smartphone during experimentation and are fed to the CNN network. The top four results predicted by the network are depicted on the smartphone screen in the audio and the visual form i.e. predicted object name and the probability of predicted object being the one actually clicked in the decreasing order of probabilities. The accuracy obtained in object recognition is about 93% through the application.
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Acknowledgements
We sincerely thank to all panel members for their guidance and encouragement in carrying out this work. We also highly indebted to Walchand college of Engineering Sangli for providing necessary information regarding this research and financial support to carry out this work.
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Kamble, K., Kulkarni, H., Patil, J., Sukhatankar, S. (2018). Object Recognition Through Smartphone Using Deep Learning Techniques. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P. (eds) Soft Computing Systems. ICSCS 2018. Communications in Computer and Information Science, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_27
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DOI: https://doi.org/10.1007/978-981-13-1936-5_27
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