NU-LiteNet: Mobile Landmark Recognition using Convolutional Neural Networks
This work addresses the challenge of efficient image recognition on mobile devices, though it appears incremental as it builds upon existing models like SqueezeNet.
The paper tackled the problem of latency and accuracy in on-device mobile landmark recognition by developing NU-LiteNet, a convolutional neural network model that is 2.6 times smaller than SqueezeNet and achieves competitive recognition accuracy on standard landmark databases.
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.