一种有效的基于卷积神经网络的车辆检索算法

车辆检索是基于图像的相似搜索的子任务,在电子商务和智能安防领域有着重要的实际应用价值。文章提出了一种有效的基于卷积神经网络车辆检索的算法。利用YOLOV2检测算法提取图片中的车辆位置减少背景对车辆检索造成的影响。提出了改变训练集中车辆的颜色进行数据增强,缓解训练数据集较少的问题。基于微调的Res Net50网络提取车辆的特征用来进行相似匹配。在香港大学车辆数据集上的实验结果表明文中提出的算法取得了不错的检索效果。

Vehicle retrieval is a sub-task of similar search based on image, which has important practical application value in the field of e-business and intelligent security. This paper presents an effective vehicle retrieval algorithm based on convolution neu-ral network. YOLOV2 detection algorithm is used to extract the vehicle position in the image to reduce the impact of background on the vehicle retrieval. The problem of changing the color of vehicle in training concentration to enhance the data is put forward to al-leviate the problem of less training data set. The ResNet50 network based on fine tuning is used to extract vehicle features for simi-lar matching. The experimental results on the vehicle data set of the University of Hong Kong show that the proposed algorithm has achieved good retrieval results.