NU-LiteNet: Mobile Landmark Recognition using Convolutional Neural Networks

TL;DR

NU-LiteNet is a compact convolutional neural network designed for mobile landmark recognition, achieving high accuracy with significantly reduced model size suitable for smartphones.

Contribution

The paper introduces NU-LiteNet, a new CNN architecture that is 2.6 times smaller than SqueezeNet while maintaining competitive recognition accuracy.

Findings

NU-LiteNet's model size is 2.6 times smaller than SqueezeNet.

NU-LiteNet achieves comparable accuracy to recent deep neural networks.

Experiments on standard landmark databases validate NU-LiteNet's effectiveness.

Abstract

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.