A Cascaded Deep Convolutional Network for Vehicle Logo Recognition From Frontal and Rear Images of Vehicles

Abstract

Vehicle logo recognition provides an important supplement to vehicle make and model analysis. Some of the existing vehicle logo recognition methods depend on the detection of license plates to roughly locate vehicle logo regions using prior knowledge. The vehicle logo recognition performance is greatly affected by the license plate detection techniques. This paper presents a cascaded deep convolutional network for directly recognizing vehicle logos without depending on the existence of license plates. This is a two-stage processing framework composed of a region proposal network and a convolutional capsule network. First, potential region proposals that might contain vehicle logos are generated by the region proposal network. Then, the convolutional capsule network classifies these region proposals into the background and different types of vehicle logos. We have evaluated the proposed framework on a large test set towards vehicle logo recognition. Quantitative evaluations show that a detection rate, a recognition rate, and an overall performance of 0.987, 0.994, and 0.981, respectively, are achieved. Comparative studies with the Faster R-CNN and other three existing methods also confirm that the proposed method performs effectively and robustly in recognizing vehicle logos of various conditions.