Open AccessModified KNN-LVQ for Stairs Down Detection Based on Digital Image
Author(s) -
Ahmad Wali Satria Bahari Johan,
Sekar Putri,
Granita Hajar,
Ardian Yusuf Wicaksono
Publication year - 2021
Publication title -
lontar komputer/lontar komputer
Language(s) - English
Resource type - Journals
eISSN - 2541-5832
pISSN - 2088-1541
DOI - 10.24843/lkjiti.2021.v12.i03.p02
Subject(s) - stairs , learning vector quantization , computer science , obstacle , artificial intelligence , computer vision , digital image , image processing , image (mathematics) , pattern recognition (psychology) , engineering , artificial neural network , geography , civil engineering , archaeology
Persons with visual impairments need a tool that can detect obstacles around them. The obstacles that exist can endanger their activities. The obstacle that is quite dangerous for the visually impaired is the stairs down. The stairs down can cause accidents for blind people if they are not aware of their existence. Therefore we need a system that can identify the presence of stairs down. This study uses digital image processing technology in recognizing the stairs down. Digital images are used as input objects which will be extracted using the Gray Level Co-occurrence Matrix method and then classified using the KNN-LVQ hybrid method. The proposed algorithm is tested to determine the accuracy and computational speed obtained. Hybrid KNN-LVQ gets an accuracy of 95%. While the average computing speed obtained is 0.07248 (s).