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

Dublin Core

Title

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

Subject

image identification, learning vector quantization, morphological features

Description

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.

Creator

Rohmat Indra Borman1
, Yusra Fernando2
, Yohanes Egi Pratama Yudoutomo3

Publisher

Universitas Teknokrat Indonesia

Date

30-04-2022

Contributor

Fajar bagus W

Format

PDF

Language

Indonesia

Type

Text

Files

Collection

Citation

Rohmat Indra Borman1 , Yusra Fernando2 , Yohanes Egi Pratama Yudoutomo3, “Identification of Vehicle Types Using Learning Vector Quantization
Algorithm with Morphological Features,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9159.