基于胶囊神经网络的车型精细识别研究

车辆型号精细识别在智能交通系统、涉车刑侦案件侦破等方面具有十分重要的应用前景.针对车辆型号种类繁多、部分型号区分度小等带来的车辆型号精细分类困难的问题,提出一种基于胶囊神经网络(capsule network,CapsNet)的车型图像识别模型CapCar.以CompCars数据集作为样本,首先通过加权平均值法进行图像...

Research on Fine Identification of Vehicle Type Based onCapsule Neural Network

The fine identification of vehicle type has an important application prospect in intelligent transportation systems and the investigation of criminal cases involving vehicles.Aiming at the difficulty of fine classification of vehicle models caused by the wide variety of vehicle models and the small degree of differentiation of some models,we propose a vehicle image recognition model CapCar based on capsule network(CapsNet).Taking the CompCars data set as a sample,first of all,the image gray processing is performed by the weighted average method to reduce the training calculation amount of the data set and improve the training speed of the model.Then all the features and local features of the vehicle model image are extracted through the capsule neural network to realize fine identification of vehicle type.Compared with the existing precise identification method of vehicle models,the proposed method effectively reduces the model parameter scale while improving the identification accuracy.The results of a large number of experiments under the benchmark dataset CompCars show that the CapCar model can achieve a fine vehicle recognition accuracy of 98.89%,and its recognition rate is higher than some other classic network models.The CapCar model parameter size is only 6.3 MB.The proposed algorithm has a certain degree of advancement.