Method of object detection for vehicle on-board video based on RetinaNet

摘要

#$%^&*AU2020100048A420200213.pdf#####ABSTRACT This invention lies in the field of digital signal processing. It is an image recognition system of obstacles and road signs around automated driving vehicles based on deep learning. The invention consists of the following steps: Firstly, we collected images from cameras on several cars. Secondly, after selecting and preprocessing the images, they were divided into two data sets: one for training, the other for testing. We then put the training data set into the convolutional neural network. In order to reach the best performance, we adjusted some parameters of the network, and finally, we put the test data set into the network and the accuracy of recognition reached. In conclusion, this system can recognise different types of obstacles and road signs with high accuracy without human intervention. 1Download the RetinaNet training model and Pascal VOC 2007 data set Divide the Pascal VOC 2007 set into training and testing data sets Initialize the neural network Adjust parameters to train the network Do the test using the testing data set Figure 1 I subnet class--box WxH W Wx sobress x256 x5 K subnet Wo * I ubnet ) c ResNet (b) feature pyramid net (c) cass subnet (top) (d) box subnet (bottom) Figure 2 1