基于改进的FAST R-CNN的前方车辆检测研究

摘要/Abstract

摘要： 目前,前方车辆检测的研究主要通过机器学习的方法,然而其难以解决遮挡和误检的问题。在这种背景下,使用深度学习的方法检测前方车辆更为有效。首先采用了选择性搜索方法获得样本图像的候选区域,然后使用改进的FAST R-CNN训练网络模型,检测道路前方车辆。已在KITTI车辆公共数据集上对该方法进行了测试,实验结果表明,所提方法的检测率高于CNN直接检测的结果,很大程度上解决了遮挡和误检的问题。而且,与先提取Harr-Like特征然后利用Adaptive Boosting分类器的算法相比,该方法在TSD-MAX交通场景数据库测试中实现了较高的性能。结果表明,该方法提高了车辆检测的准确性和鲁棒性。

关键词: 卷积神经网络, 前方车辆检测, 样本图像, 准确率

Abstract: The current research on vehicle detection is mainly about machine learning,but it is still difficult to deal with occlusion and false detection.In this paper,using deep learning methods to detect forward vehicles is more effective.This paper firstly adopts the selective search method to obtain the candidate area of the sample image,and then uses the improved FAST R-CNN training network to detect the forward vehicles on the road.The method has been tested in the KITTI vehicle public dataset.The experimental results show that the detection rate of this method is higher than that of the direct test based on CNN.The problem of occlusion and error detection is largely solved.Moreover,the widely used method extracts the circulated Harr-Like features,and then uses the Adaptive Boosting classifier algorithm.Compared in TSD-MAX traffic scene dataset,the proposed method provides a higher performance.The results show that this method improves the accuracy and robustness of vehicle detection.

Key words: Accurate rate, Convolutional neural network(CNN), Forward vehicle detection, Sample image

中图分类号:

TP311

史凯静,鲍泓. 基于改进的FAST R-CNN的前方车辆检测研究[J]. 计算机科学, 2018, 45(6A): 179-182. https://doi.org/

SHI Kai-jing, BAO Hong. Forward Vehicle Detection Research Based on Improved FAST R-CNN Network[J]. Computer Science, 2018, 45(6A): 179-182. https://doi.org/

参考文献

[1]林晓翠.基于深度学习的车辆检测研究[D].厦门:厦门大学,2016.
[2]TANG Y,ZHANG C Z,GU R S,et al.Vehicle detection and recognition for intelligent traffic surveillance system[J].Multimedia Tools & Applications,2017,76(4):5817-5832.
[3]郑胤,陈权崎,章毓晋.深度学习及其在目标和行为识别中的新进展[J].中国图象图形学报,2014,19(2):175-184.
[4]UIJLINGS J R R,SANDE K E A,GEVERS T,et al.Selective Search for Object Recognition[J].International Journal of Computer Vision,2013,104(2):154-171.
[5]ZHANG F F,BAO H,WANG D.A Revised Multi-lane Vehicle Detection Model with Adaptive Boosting Algorithm[J].Journal of Residuals Science & Technology,2016,13(8):1-6.
[6]QIU Q J,LIU Y,CAI D W.Vehicle detection based on LBP features of the Haar-like Characteristics[C]∥Intelligent Control and Automation.IEEE,2015:1225-1227.
[7]张全发,蒲宝明,李天然,等.基于HoG特征和机器学习的工程车辆检测[J].计算机系统应用,2013,22(7):104-107.
[8]ZHANG Y Z,SUN P F,LI J F,et al.Real-time vehicle detection in highway based on improved Adaboost and image segmentation[C]∥IEEE International Conference on Cyber Technology in Automation,Control,and Intelligent Systems.IEEE,2015:2006-2011.
[9]曹诗雨,刘跃虎,李辛昭.基于Fast R-CNN的车辆目标检测[J].中国图象图形学报,2017,22(5):671-677.
[10]REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:To- wards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137.
[11]KONOPLICH G V,PUTIN E,FILCHENKOV A A.Application of deep learning to the problem of vehicle detection in UAV images[C]∥IEEE International Conference on Soft Computing and Measurements.2016:4-6.
[12]CAi Z W,FAN Q F,FERIS R S,et al.A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection[M]∥Computer Vision-ECCV 2016.Springer International Publishing,2016.
[13]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥CVPR.2014.
[14]GEIGER A,WOJEK C,SAARBRUCKEN M,et al.Joint 3d estimation of objects and scene layout[C]∥NIPS,2011.2011:1467-1475.
[15]FAN Y,CHOI W G,LIN Y Q.Exploit all the layers:Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers[C]∥Computer Vision and Pattern Recognition.IEEE,2016:2129-2137.
[16]DOLL′AR P,WOJEK C,SCHIELE B,et al.Pedestrian Detection:An Evaluation of the State of the Art[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,34(4):743-761.
[17]刘洋,王海晖,向云露,等.基于改进的Adaboost算法和帧差法的车辆检测方法[J].华中科技大学学报(自然科学版),2013,41(s1):379-382.

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