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

摘要

信息安全已成为人工智能领域处理大数据的重要课题。大多数系统都针对普通道路上的障碍物检测。在本文中，我们提出了一种基于卷积神经网络（CNN）的野外环境障碍物检测方法。首先，我们提出了一种感兴趣区域（ROI）提取算法来处理可疑的障碍物区域。其次，我们设计了一个 CNN 模型来对提取的候选区域的特征图进行分类。实验结果表明，该方法识别准确率高，能有效检测障碍物。

缩写： CNN：卷积神经网络；ROI：感兴趣区域；DBM：深度玻尔兹曼机；AE：自动编码器；RPN：区域提案网络；ReLU：整流线性单元；RCNN：具有 CNN 特征的区域；VGG-Net：视觉几何组网