Research On Vehicle Logo Detection Based On The Convolutional Neural Network

As a key sign of the vehicle,vehicle logos are not easily modified,which play important auxiliary roles in the extraction of vehicle information.Traditional vehicle logo detection algorithms based on handcrafted descriptors mostly have low accuracy and slow speed.Most object detection algorithms based on deep learning cannot maintain a balance between speed and accuracy.Based on the convolutional neural network(CNN),we carry out research on the vehicle logo detection,and two improved algorithms are proposed: a fast vehicle logo detection algorithm based on YOLOv3 and a Center Net-based vehicle logo detection algorithm.Our experiments are carried out on the dataset constructed from images captured on the highway.The experimental results show that our algorithms proposed in this paper have certain improvements in speed and accuracy,which make them more suitable for practical monitoring scenarios.The main works of this thesis are listed as follows:(1)Existing vehicle logo detection methods are summarized and analyzed.The object detection algorithms based on traditional descriptors and CNNs are summarized,and their application in vehicle logo detection are analyzed.Also,some common methods such as data augmentation,backbone network and feature pyramid involved in object detection are summarized.(2)An improved vehicle logo detection algorithm YVLDet based on YOLOv3 is proposed.The algorithm uses depthwise convolution,dilated convolution and channel attention module to construct a faster backbone network;combined with deformable convolution and transposed convolution,it designs a more effective up sampling strategy;it use shallower features in the backbone network to enhance the spatial feature of the detection branches,so that the detected feature map contains rich spatial feature information;finally,combining the detection module of YOLOv3,two detection branches of different scales are used for detection.The experimental results show that the algorithm can improve the detection speed while ensuring the detection accuracy.(3)A Center Net-based vehicle logo detection algorithm CVLDet is proposed.First,aiming at the shortcoming of small number of vehicle logos in the dataset,a data augmentation method is proposed to improve the learning ability of the network.Then,it expands the network width of Res Net-18 and fuses shallow network features to increase the spatial information of detection branches.Finally,it fuses input image feature and deep network feature to reduce the abstraction of the deep network feature,and make the spatial information of the detection feature map richer.Experimental results show that this method can improve the accuracy while ensuring the speed of vehicle logo detection.