earticle

영어

Immature green citrus fruit detection using conventional color images is a challenging task due to fruit color similarity with the background, partial occlusion, varying illumination and shape irregularity. Therefore, most existing green fruits detection algorithms, which use color as the main discriminant feature, have a low recognition rate and a high rate of false positives. In this manuscript, we developed a novel Green Citrus fruit Detection algorithm based on the proposed Reticulate Grayladder Feature (GCDRGF), which contained 4 major steps: First, an 8-graylevel image was generated by the preprocessing steps of median filtering, histogram-based equalization and 8-graylevel discretization of the input raw image. Secondly, reticulate grayladders were obtained by a multidirectional scanning on the 8-graylevel image, and rule-based pseudo-grayladder removal strategies were used to remove false positives of target grayladders. Thirdly, grayladder clustering and fruit location fitting were used to generate candidate regions for target fruits. Finally, majority voting was performed to determine the results of candidate regions based on the analysis of apparent features and recticulate grayladders within candidate regions. The experimental results proved the effectiveness of the proposed reticulate grayladder feature and the corresponding detection algorithm with respect to various illuminant and imaging conditions. Compared with the existed eigenfruit algorithm, our algorithm has a higher rate of successful recognition and a lower rate of false positives, which helps to greatly improve the productivity of robotic operations.