基于VGGNet颜色识别的车辆检索系统设计

Design of Vehicle Retrieval System Based on VGGNet Color Recognition

深度学习中的卷积神经网络对图像的平移、旋转等变换有很强的抗干扰能力,它与传统的车辆识别技术相比能提取到更深层次、更丰富的图像信息。基于VGGNet网络结构,并模拟人眼对车辆特征感知的先后顺序,设计了一个对车辆图像库中的车辆数据进行分级检索的系统。首先搭建并训练一个可进行8种颜色识别的卷积神经网络,对目标车辆进行颜色识别,再融合SIFT和LBP特征对颜色相同的候选车辆数据库进行特征匹配与检索。该系统的分级检索模式能够有效地缩小检索范围,提高检索效率,多特征的融合也可以保障提取足够多的图像信息,确保检索精度。

The Convolution Neural Network(CNN)in Deep Learning has a strong anti-jamming ability for image translation,rotation and other transformations.Compared with the traditional vehicle recognition technology,it can extract deeper and richer image information.Based on the VGGNet structure and simulating the order of human eyes'perception of vehicle characteristics,this paper designs a hierarchical retrieval system for vehicle image database.Firstly,a CNN which can recognize eight kinds of colors is constructed and trained to recognize the color of the target vehicle.Then,SIFT and LBP features are combined to match and retrieve the same color candidate vehicle database.The hierarchical retrieval mode of the system can effectively reduce the scope of retrieval and improve the efficiency of retrieval.The fusion of multi features can also guarantee the extraction of enough image information and ensure the accuracy of retrieval.