A light-weighted convolutional neural network for image classification in autopilot system

Abstract

Autopilot has been a heated technology in recent days, and it is used in plenty of car brands. People are becoming dependent on autopilot technology. However, before this technology becomes mature, there are still a lot of insufficiencies that need to improve. The focus of this essay is on the object detection shortage that exists in the autopilot system. This essay is first going to introduce a lite CNN-based method to improve the object detection and identification module. In the result part of the essay, the essay is going to show the result of the training and some new prediction function will be implemented. Also, some accuracy improving methods including data augmentation and Dropout will be introduced, and the effect of the improving methods will be shown in the essay. After training, the autopilot system is going to distinguish pedestrians from vehicles. Although the road condition is far more complex than just vehicles and pedestrians, for convenience this essay is only going to implement the two datasets. Also, some further improvement intension will be introduced.