Genetic input selection to a neural classifier for defect classification of radiata pine boards

A genetic algorithm was used to determine an appropriate set of features for automatic defect classification of radiata pine boards. The study was performed using a low-cost machine vision system composed of a color video camera, a frame grabber, and a microcomputer. The following 10 defect categories were considered, plus clear wood: birds eye & freckle, bark & pitch pockets, wane, split, blue stain, stain, pith, dead knot, live knot, and hole. A database was built containing color images of 2,958 board faces. A total of 16,800 feature vectors were extracted from these images, and partitioned into training, validation, and test sets. Each vector was composed of 182 features measured in the segmented objects and in windows around the objects. By using feature selection algorithms, 64 out of 182 original features were selected and used as inputs to a multilayer perceptron neural network classifier, without reducing the classification performance. Using the set of features evolved by a genetic algorithm, the best off-line performance obtained was 74.5 percent of correct classifications on the test set. The classification performance on a reduced database with 7 defect categories reached 87.8 percent. An online system evaluation yielded 80 percent of correct classifications with 10 defect categories plus clear wood. The study shows that the genetic selection of features allows us to identify the most relevant features for complex classification problems, such as wood defect classification, where the best features are unknown.