The growth of high-performance mobile devices has resulted in more research into on-device image recognition. The research problems are the latency and accuracy of automatic recognition, which remains obstacles to its real-world usage. Although the recently developed deep neural networks can achieve accuracy comparable to that of a human user, some of them still lack the necessary latency. This paper describes the development of the architecture of a new convolutional neural network model, NU-LiteNet. For this, SqueezeNet was developed to reduce the model size to a degree suitable for smartphones. The model size of NU-LiteNet is therefore 2.6 times smaller than that of SqueezeNet. The recognition accuracy of NU-LiteNet also compared favorably with other recently developed deep neural networks, when experiments were conducted on two standard landmark databases.
@article{arxiv.1810.01074,
title = {NU-LiteNet: Mobile Landmark Recognition using Convolutional Neural Networks},
author = {Chakkrit Termritthikun and Surachet Kanprachar and Paisarn Muneesawang},
journal= {arXiv preprint arXiv:1810.01074},
year = {2018}
}
Comments
6 pages, 7 figures, this paper presented to NVIDIA's GPU Technology Conference (GTC 2017), San Jose McEnery Convention Center, San Jose, CA