关键词: 车辆检测, Faster R-CNN模型, 区域建议网络, 难负样本挖掘, KITTI数据集

Abstract: Since the traditional machine learning methods are easy to be affected by light, target scale and image quality in vehicle detection applications, resulting the low efficiency and generalization ability, a vehicle detection method based on improved Faster Regions with Convolutional Neural Network features (R-CNN) model was proposed. On the basis of Faster R-CNN model, through convolution and pooling operations to extract the features of vehicles, by combining with multi-scale training and hard negative sample mining strategy to reduce the influence of complex environment, the KITTI data set was used to train the deep neural network model, and the images were collected from actual scene to test the trained neural network model. In the simulation experiments, while the detection time was guaranteed, the detection accuracy of the proposed method was improved by about 8% compared to the original Faster R-CNN algorithm. The experimental results show that the proposed method can automatically extract the features of vehicles, solve the time-consuming and laborious problem of extracting features by traditional methods, effectively improve the accuracy of vehicle detection, and has good generalization ability and wide range of applications.

Key words: vehicle detection, Faster Regions with Convolutional Neural Network features (R-CNN) model, region proposal network, hard negative sample mining, KITTI data set