Earticle

초록

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.

목차

Abstract
 1. Introduction
 2. Material and Algorithm
  2.1. Image Acquisition
  2.2. Overview of the Proposed Algorithm
  2.3. Preprocessing
  2.4. Multidirectional Scanning of Grayladders
  2.5. Rule-Based Pseudo-Grayladders Removal
  2.6. Candidate Fruit Region Generation
  2.7. Pseudo-Fruit Removal by Majortity Voting
 3. Results and Discussions
  3.1. Qualitative Results
  3.2. Quantitative Results
 4. Conclusions
 References

키워드

저자

• Mingjun Wang [ Department of mechanical engineering, Ningbo University of Technology, Ningbo, China / Jiangsu key laboratory for intelligent agricultural equipment, Nanjing Agricultural University, Nanjing, China ]
• Jun Zhou [ Jiangsu key laboratory for intelligent agricultural equipment, Nanjing Agricultural University, Nanjing, China ]
• Weiyan Shang [ Department of mechanical engineering, Ningbo University of Technology, Ningbo, China ]
• Rufu Hu [ Department of mechanical engineering, Ningbo University of Technology, Ningbo, China ]
• Xuefeng Wang [ Department of mechanical engineering, Ningbo University of Technology, Ningbo, China ]
• Liang Gong [ Department of mechanical engineering, Shanghai Jiaotong University, Shanghai, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605