Multi-class fruit detection based on multiple color channels

Abstract

In this paper a novel approach based on multiple color channels is proposed for multi-class fruit detection. Multi-feature fusion of global color histogram, Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG), and LBP based on Gabor wavelets (GaborLBP) is utilized to improve fruit recognition. These four features are extracted on multiple color channels. To select the optimal features of HOG, LBP and GaborLBP in each color channel, the optimal block is selected according to the cross validation accuracy. The optimal color channel fusion is optimized by a simple optimal color channel selection framework proposed in this paper. A multi-class fruit classifier is trained using the optimal color channel fusion and LibSVM. Besides, the proposed method is evaluated on a fruit dataset including 5 classes of fruit and 1778 test images. The experiments show that the proposed multi-class fruit detection method is effective. This proposed method has produced 0.156 miss rate at 0.5427 false positives per image (FPPI) and high average precision (0.8135), and can detect multi-class and multiple fruits in a variety of backgrounds, locations, angles, and sizes.