Abstract
In this paper, a CNN-based vehicle detection and retrieval framework is proposed for the intelligent transportation system. Firstly, the vehicle target is detected from the traffic scene. The proposed object detection method uses a fully convolutional neural network (CNN) based on SqueezeNet, which has the characteristics of real-time, high accuracy and has small model size. Secondly, an intra-class image retrieval method is presented to search vehicles which are similar to the target vehicle in the dataset. The image retrieval results can be used for traffic scenes simulation and modeling. The experiments and comparisons prove the effectiveness of our framework.
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