A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification
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Date
2024-02
Authors
Dembele, Adama
Mwangi, Ronald Waweru
Kube, Ananda Omutokoh
Journal Title
Journal ISSN
Volume Title
Publisher
Scientific Research Publishing
Abstract
Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which
employs depthwise convolution to reduce network complexity. MobileNetV1
employs a stride of 2 in several convolutional layers to decrease the spatial
resolution of feature maps, thereby lowering computational costs. However,
this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance,
a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated
separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively
expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF)
module that uses parallel multi-resolution branches architecture to process
the input feature map in order to extract the multi-scale feature information
of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the
network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.
Description
Article
Keywords
MobileNet, Image Classification, Lightweight Convolutional Neural Network, Depthwise Dilated Separable Convolution, Hierarchical Multi-Scale Feature Fusion
Citation
Dembele, A., Mwangi, R. W., & Kube, A. O. (2024). A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification. Journal of Computer and Communications, 12(2), 173-200.