农机化研究
農機化研究
농궤화연구
JOURNAL OF AGRICULTURAL MECHANIZATION RESEARCH
2014年
1期
178-183
,共6页
吴定中%邹湘军%熊俊涛%陈丽娟%彭红星
吳定中%鄒湘軍%熊俊濤%陳麗娟%彭紅星
오정중%추상군%웅준도%진려연%팽홍성
机器视觉%复杂背景%柑橘%识别
機器視覺%複雜揹景%柑橘%識彆
궤기시각%복잡배경%감귤%식별
machine vision%complex background%citrus%recognition%location
面向复杂背景环境的定位目标的快速识别是野外作业机器人的关键技术,以柑橘为例,研究了自然环境下基于彩色信息的目标定位的识别方法。首先,采用 YCbCr 颜色模型来分析柑橘彩色图像的颜色和灰度特征,并通过Otsu 与FCM 分割算法相结合对在不同光照条件下拍摄的彩色目标图像进行分割;然后,利用形态学数学和区域标记消除分割后产生的随机噪声;最后,用凸包算法提取果实形状特征,并通过凸包算法来判定是否为柑橘和是否可采。对500张彩色柑橘图像进行分割,结果表明采用 Cr 颜色分量和 Otsu 与 FCM 算法相结合有效地解决复杂自然光照下的分割问题;对963个柑橘进行了凸包算法识别试验,总体识别率达87.53%。凸包算法对遮挡图像也可进行高效识别,并能快速、准确地提取柑橘目标的质心坐标。
麵嚮複雜揹景環境的定位目標的快速識彆是野外作業機器人的關鍵技術,以柑橘為例,研究瞭自然環境下基于綵色信息的目標定位的識彆方法。首先,採用 YCbCr 顏色模型來分析柑橘綵色圖像的顏色和灰度特徵,併通過Otsu 與FCM 分割算法相結閤對在不同光照條件下拍攝的綵色目標圖像進行分割;然後,利用形態學數學和區域標記消除分割後產生的隨機譟聲;最後,用凸包算法提取果實形狀特徵,併通過凸包算法來判定是否為柑橘和是否可採。對500張綵色柑橘圖像進行分割,結果錶明採用 Cr 顏色分量和 Otsu 與 FCM 算法相結閤有效地解決複雜自然光照下的分割問題;對963箇柑橘進行瞭凸包算法識彆試驗,總體識彆率達87.53%。凸包算法對遮擋圖像也可進行高效識彆,併能快速、準確地提取柑橘目標的質心坐標。
면향복잡배경배경적정위목표적쾌속식별시야외작업궤기인적관건기술,이감귤위례,연구료자연배경하기우채색신식적목표정위적식별방법。수선,채용 YCbCr 안색모형래분석감귤채색도상적안색화회도특정,병통과Otsu 여FCM 분할산법상결합대재불동광조조건하박섭적채색목표도상진행분할;연후,이용형태학수학화구역표기소제분할후산생적수궤조성;최후,용철포산법제취과실형상특정,병통과철포산법래판정시부위감귤화시부가채。대500장채색감귤도상진행분할,결과표명채용 Cr 안색분량화 Otsu 여 FCM 산법상결합유효지해결복잡자연광조하적분할문제;대963개감귤진행료철포산법식별시험,총체식별솔체87.53%。철포산법대차당도상야가진행고효식별,병능쾌속、준학지제취감귤목표적질심좌표。
Fast recognition of target for complex background was the key technology of the field robot .Take citrus as ex-ample , the recognition method was researched based on color information in the natural environment .First of all , the YCbCr color model is used to analyze the color and grayscale characteristics of the citrus color image .Color target image in different lighting conditions will be segmented by Otsu and FCM segmentation algorithm ;Then, morphological mathe-matics and regional mark eliminate the random noise generated in the segmented;Finally , the convex hull algorithm will extract characteristics of fruit shape ,and convex hull algorithm will determine whether citrus can be picked .500 color cit-rus images are segmented , the results show that the Cr color components and Otsu Combined with FCM algorithm effec-tively solve the segmentation problem under complex natural light; By testing 963 citrus recognition used by the convex hull algorithm ,the rate of overall recognition have achieved 86 .3 %, and the recognition rate of citrus overlapped exceed over 87 .6 percent , this indicated that the shape feature extraction methods based on convex hull algorithm , effectively recognited images of citrus overlapped and blocked and the citrus center of mass coordinates of the target can be quickly and accurately extracted .