Algorithm of vehicle detection based on RCNN
                                                   ZHU Maotao1, ZHANG Hongxiang1,2, FANG Ruihua2
(1.School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013，China;2.Shanghai Ganxiang Automobile Mirror Industry, Shanghai 201518, China)

Abstract: Aiming at improving the robust adaptation and realtime of the vehicle detection algorithm based on traditional machine learning, vehicle detection algorithm based on deep learning was researched. The principle of Faster RCNN detection algorithm was analyzed. Based on TensorFlow deep learning frame and made use of Python programming language, the Faster RCNN algorithm was realized. The road condition data set of four seasons was collected and labeled, 12 000 pictures was included. The data set was pretreat by three different method and the algorithm parameter of Faster RCNN was tuned by controlled experiment. The accuracy and speed of four detection algorithm, RCNN, SPPnet, Fast RCNN and Faster RCNN, was compared by variablecontrolling approach. The results indicate that the speed of vehicle detection based on Faster RCNN is 69 ms and the accuracy rate is 91.3%，the algorithm can realize realtime and high accuracy of vehicle detection.

Key words: vehicle engineering; assistance driving; vehicle detection; deep learning; region proposal networks; convolutional neural networks