Obstacle detection in a field environment based on a convolutional neural network security

Abstract

ABSTRACT Information security has become an important subject in the artificial intelligence filed to handle big data. Most of the systems aim at obstacle detection on ordinary roads. In this paper, we proposed a method for detecting obstacles in a field environment based on convolutional neural network (CNN). Firstly, we propose a region of interest (ROI) extraction algorithm to deal with the suspected obstacle area. Secondly, we design a CNN model to classify the extracted feature maps of candidate areas. The experimental results indicate that the proposed method has high recognition accuracy and can detect obstacles effectively. Abbreviations: CNN: Convolutional Neural Network; ROI: Region of Interest; DBM: Deep Boltzmann Machines; AE: Auto-encoders; RPN: Region Proposal Network; ReLU: Rectified Linear Unit; RCNN: Regions with CNN Features; VGG-Net: Visual Geometry Group Net