Abstract
Fine-grained Vehicle Classification Technology Based on Fusion of Multi-convolutional Neural Networks
Highlights
As an application field of target detection, vehicle detection has been widely used in many industries
We attempt to solve the problem of fine-grained vehicle classification through deep learning
We studied fine-grained vehicle classification technology based on the fusion of multi-convolutional neural networks (CNNs)
Summary
As an application field of target detection, vehicle detection has been widely used in many industries. Prokaj and Medioni[14] used a 3D model of a vehicle to perform vehicle pose estimation, projected it onto the 2D plane, and used the scale-invariant feature transform (SIFT) operator to compare different vehicles so as to classify vehicle finegrain size. Their approach can solve the problem of inaccurate classification. We attempt to solve the problem of fine-grained vehicle classification through deep learning
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