Fine-Tuned Convolutional Neural Networks for Bangladeshi Vehicle Classification
2022 International Conference on Innovations in Science, Engineering and Technology (ICISET)(2022)
摘要
Classification of vehicles plays an important role in the intelligent transport system. In this paper, we propose a framework for the classification of Bangladeshi vehicle images based on fine-tuned convolutional neural networks (CNNs). Using the proposed framework and an unified experimental setting, we fine-tuned fifteen popular CNN architectures, namely, AlexNet, Inception-V3, VGG-II, VGG-13, VGG-16, VGG-19, ResNet18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, DenseNet121, DenseNet-161, DenseNet-169 and DenseNet-201 pretrained on ImageNet dataset on a public Bangladeshi vehicle image dataset. We conduct a systematic and comprehensive analysis on the performance of the fine-tuned CNNs which leads to new insights. Our experimental results show that ResNet-152 and DenseNet-201 fine-tuned with our proposed strategy provide excellent performance for the classification of Bangladeshi vehicle images. Our implementation is available at https://github.con MinhajurRFahad/bd-vehicle-cnn-benchmark.
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关键词
Vehicle classification,transfer learning,deep learning,convolutional neural network,Bangladeshi vehicle classification
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