Classification method for SRC wooden board defects based on single threshold segmentation of artificial bee colony
WEI Xiao-hui
MA Xiao-zhen
LIU Ya-qiu
Abstract：Aiming at such shortages as easily falling into local optimum situation, precocity and slow convergence speed of traditional single threshold segmentation algorithm for wooden board defects, a single threshold segmentation algorithm based on improved artificial bee colony (ABC) algorithm was proposed.In order to improve the defect classification accuracy and reduce the computational work, the sparse representation-based classifier (SRC) was applied in the classification process of wooden board defects.The improved algorithm simultaneously could realize both global and local search during each iteration, and the bee scouts could select nectar resources randomly in global area to speed up the convergence rate.The search radius was adaptively adjusted according to time-varied search parameters, and the SRC transformed the defect classification problem into the problem of obtaining the most sparse coefficient solution.The results show that the proposed algorithm can compute the optimal segmentation threshold, improve the classification accuracy to above 90%, and has certain reliability and feasibility.
Keywords：wooden board defectartificial bee colony algorithmsingle threshold segmentationnectar resourcesparse representation-based classifiersearch radiustime-varied searching parametermost sparse coefficient
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 292-298 )