Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features

Abstract

The increase in the number of vehicles every year results in traffic jams. So it is necessary to identify the type of vehicle, so that the vehicle can be arranged according to the path. This study aims to develop a system that can identify the type of vehicle using the Learning Vector Quantization (LVQ) algorithm. In order for LVQ to work well in identifying, information in the form of characteristics of the object is needed. For this reason, the LVQ algorithm is combined with morphological feature extraction using the parameters of area, circumference, eccentricity, major axis length, and minor axis length to obtain shape features. Based on the test results using a confusion matrix by calculating precision, recall and accuracy, it is obtained that the precision value is 85%, recall is 82% and accuracy is 83%. This paper shows that for vehicle identification, the combination of morphological feature extraction and LVQ algorithm produces a model that can identify vehicles based on their shape and classify classes through competitive layers that are supervised by a single layer network architecture, this makes the computational process faster and does not burden the computational process.