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A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow

Wei Wang¹, Yiyang Hu¹, Ting Zou²

¹College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Computational Intelligence and Neuroscience

|August 18, 2020

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This summary is machine-generated.

Deep neural networks (DNNs) are challenging for embedded systems. Dilated-MobileNet improves accuracy by expanding receptive fields without adding parameters, enhancing deep learning on resource-constrained devices.

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep neural networks (DNNs) are computationally intensive, limiting their use in embedded systems.
Model compression and acceleration are crucial for deploying DNNs on resource-constrained hardware.
MobileNet utilizes depthwise separable convolutions to reduce parameters and complexity with minimal precision loss.

Purpose of the Study:

To propose improved MobileNet models, termed Dilated-MobileNet, for enhanced performance in embedded systems.
To investigate the impact of incorporating dilated convolutions into MobileNet architectures.
To achieve higher classification accuracy without increasing model size or computational load.

Main Methods:

Developed three Dilated-MobileNet models by integrating dilated convolutions into specific layers of the original MobileNet architecture.

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Focused on expanding the local receptive field of convolutional filters in shallow layers.

Evaluated model performance on Caltech-101, Caltech-256, and Tubingen animals with attribute datasets.

Main Results:

Dilated-MobileNet models demonstrated an improvement in classification accuracy compared to the standard MobileNet.
The proposed models achieved up to a 2% increase in classification accuracy.
The enhancements were realized without an increase in the number of model parameters.

Conclusions:

Dilated convolutions are an effective method for expanding receptive fields and improving DNN accuracy.
Dilated-MobileNet offers a viable solution for deploying accurate deep learning models on embedded systems.
The approach balances performance gains with the constraints of limited hardware resources.