• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2020, Vol. 42 ›› Issue (09): 1599-1607.

• 图形与图像 • 上一篇    下一篇

基于颜色与果径特征的苹果树果实检测与分级

樊泽泽,柳倩,柴洁玮,杨晓峰,李海芳   

  1. (太原理工大学信息与计算机学院,山西 晋中  030600)
  • 收稿日期:2019-12-05 修回日期:2020-03-08 接受日期:2020-09-25 出版日期:2020-09-25 发布日期:2020-09-25
  • 基金资助:
    国家自然科学基金(61976150);山西省重点研发计划(201803D31038);山西省晋中市科技重点研发计划(Y192006);赛尔网络下一代互联网技术创新项目(NGII20181206)

Apple detection and grading based on color and fruit-diameter

FAN Ze-ze,LIU Qian,CHAI Jie-wei,YANG Xiao-feng,LI Hai-fang   

  1. (College of Computer Science and Technology,Taiyuan University of Technology,Jinzhong 030600,China)

  • Received:2019-12-05 Revised:2020-03-08 Accepted:2020-09-25 Online:2020-09-25 Published:2020-09-25

摘要: 苹果是多地的主产水果和主要经济作物之一,通过自然环境下的苹果树图像对苹果检测并分级有助于推进果业现代化。结合深度学习和传统方法,提出融合颜色与果径特征的果实检测与分级算法。为提高果树图像中小目标的检出和光照不匀、果实颜色差异大时检测边框的准确率,基于卷积神经网络提出自然场景下的苹果检测算法,在2组不同尺度的特征图上进行果实检测,提取检测框内图像在CIELAB颜色空间下b*、(1.8b*-L*)颜色分量,将图像二值化并精确提取目标轮廓二次校正检测框。实验结果显示,苹果检测算法的准确率达91.60%,F1-score值达87.62%。据图像内目标大小与实际尺寸的映射方法计算苹果直径,实现果实分级,实验表明分级准确率达90%。

关键词: 苹果检测, 苹果分级, 卷积神经网络, 果径, 颜色分量

Abstract: Apple is one of the main producing fruits and the main economic crops in many areas. Detecting and grading apples through the image of apple trees under natural environment is helpful to promote the modernization of fruit industry. Combining deep learning with traditional methods, this paper proposes a fruit detection and grading method combining color and apple diameter. In order to improve the detection rate of unobvious targets and the precision of bounding boxes when illumination or fruit coloration is uneven, the convolutional neural network is used to construct an apple detection model and detect apple on feature maps of two scales. b*, (1.8b* -L*) color components of the image in bounding boxes in CIELAB color space are extracted, the image is binarized, and the target contour is accurately extracted to correct the bounding boxes. Experimental results show that the precision is 91.60% and the F1-score value is 87.62%. According to the image and actual size mapping method, the apple diameter is calculated to achieve the apple grading. Experimental results show that the grading accuracy is 90%.


Key words: apple detection, apple grading, convolutional neural network, fruit-diameter, color component