%0 Journal Article %T Deep convolutional neural networks for vehicle detection %J Agricultural equipment and vehicle engineering %D 2019 %A Ye, Yunsheng %V 57 %N 2 %P - %X This paper introduces a method of vehicle identification based on convolution neural network. Firstly, the method detects the edge and uses the lane line model to match, so as to determine the area of interest on the road. Secondly, the road video is collected to label the vehicle targets and make the vehicle data set, and then design a convolution neural network. The vehicle data set is used to train the detector so that the detector can adapt to the task of vehicle identification. Finally, the vehicle is detected in the area of interest of the road. Compared with the traditional vehicle identification method, this method has better accuracy and robustness, and has a satisfactory recognition effect under complicated driving conditions.

Language: zh

%G zh %I Nong ye zhuang bei yu che liang gong cheng za zhi she %@ 1673-3142 %U http://dx.doi.org/