Fast Road Detection Methods on a Large Scale Dataset for Assisting Robot Navigation Using Kernel Principal Component Analysis and Deep Learning

摘要

A large database needs a heavy computation when the analysis is needed. The heavy computation leads to decrease the autonomous system performance. In our previous work, a complete vision based dirvable road detection method was proposed using Deep Belief Neural Network(DBNN). However, the previous method is unable to perform in real time for a large scale database. Due to solve this problem, in this paper, two fast drivable road detection approaches have been proposed using Kernel Principal Component Analysis-Deep Belief Neural Network (KPCA-DBNN) and Dimensionality Reduction Deep Belief Neural Network (DRDBNN) to reduce heavy computation for a large database. In the KPCA-DBNN, KPCA is used for dimensionality reduction and DBNN is used for classification. In the DRDBNN, two DBNNs are used. One DBNN is used for dimensionality reduction, and other DBNN is used for classification. The performance of the two approaches is demonstrated by the experimental results. From the experimental results, we see that the KPCA-DBNN and DRDBNN approaches reduce the processing time as compared to the conventional DBNN method. In addition, the results indicate that DRDBNN performed better than KPCA-DBNN in terms of detection accuracy on a large road database.