Deep Denoising Autoencoding Method for Feature Extraction and Recognition of Vehicle Adhesion Status

Abstract

Feature extraction and classification for deep learning are studied to recognize the problem of vehicle adhesion status. Data concentration acquired by automobile sensors contains considerable noise. Thus, a sparse autoencoder (stacked denoising autoencoder) is introduced to achieve network weight learning, restore original pure signal data by use of overlapping convergence strategy, and construct multiclassification support vector machine (SVM) for classification. The sensors are adopted in different road environments to acquire data signals and recognize the adhesion status online. Results show that the proposed method can achieve higher accuracies than those of the adhesion status recognition method based on SVM and extreme learning machine.