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http://dx.doi.org/10.7471/ikeee.2019.23.4.1302

Image Classification using Deep Learning Algorithm and 2D Lidar Sensor  

Lee, Junho (Mando Corporation)
Chang, Hyuk-Jun (School of Electrical Engineering, Kookmin University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1302-1308 More about this Journal
Abstract
This paper presents an approach for classifying image made by acquired position data from a 2D Lidar sensor with a convolutional neural network (CNN). Lidar sensor has been widely used for unmanned devices owing to advantages in term of data accuracy, robustness against geometry distortion and light variations. A CNN algorithm consists of one or more convolutional and pooling layers and has shown a satisfactory performance for image classification. In this paper, different types of CNN architectures based on training methods, Gradient Descent(GD) and Levenberg-arquardt(LM), are implemented. The LM method has two types based on the frequency of approximating Hessian matrix, one of the factors to update training parameters. Simulation results of the LM algorithms show better classification performance of the image data than that of the GD algorithm. In addition, the LM algorithm with more frequent Hessian matrix approximation shows a smaller error than the other type of LM algorithm.
Keywords
Deep learning; deep learning neural network; convolutional neural network; object detection; image classification;
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