  Object classification is a significant task in computer vision. It has become
an effective research area as an important aspect of image processing and the
building block of image localization, detection, and scene parsing. Object
classification from low-quality images is difficult for the variance of object
colors, aspect ratios, and cluttered backgrounds. The field of object
classification has seen remarkable advancements, with the development of deep
convolutional neural networks (DCNNs). Deep neural networks have been
demonstrated as very powerful systems for facing the challenge of object
classification from high-resolution images, but deploying such object
classification networks on the embedded device remains challenging due to the
high computational and memory requirements. Using high-quality images often
causes high computational and memory complexity, whereas low-quality images can
solve this issue. Hence, in this paper, we investigate an optimal architecture
that accurately classifies low-quality images using DCNNs architectures. To
validate different baselines on lowquality images, we perform experiments using
webcam captured image datasets of 10 different objects. In this research work,
we evaluate the proposed architecture by implementing popular CNN
architectures. The experimental results validate that the MobileNet
architecture delivers better than most of the available CNN architectures for
low-resolution webcam image datasets.
