摘要
阴影影响下苹果目标的快速准确识别是苹果采摘机器人视觉系统必须解决的关键技术之一。为了实现阴影影响下苹果目标的准确识别,该研究采用光照无关图理论实现了苹果表面阴影的去除。以自然场景下获取的受不同程度阴影影响的苹果目标图像为研究对象,首先利用光照无关图原理获取阴影苹果图像的光照无关图,达到突出苹果目标阴影区域的目的;其次提取原图像的红色分量信息并与关照无关图进行相加处理;最后将相加后的图像进行自适应阈值分割处理,达到去除阴影的目的。为了验证该算法的有效性与准确性,利用20幅受阴影影响的苹果目标图像进行了试验,并与Otsu算法、1.5*R-G色差算法进行了对比,试验结果表明:Otsu算法仅能识别出未受阴影影响的苹果区域;1.5*R-G色差算法受光照影响较大,对于苹果图像的相对强光照区域和部分阴影区域不能有效识别;基于光照无关图的苹果表面阴影去除方法对阴影影响下的苹果目标图像分割效果较好,可以克服光照过强的问题,并准确识别出阴影影响下的苹果目标。文中算法的平均假阳性率为17.49%,比Otsu算法降低了52.84%,比1.5*R-G算法降低了26.18%;文中算法的平均重叠系数为86.59%,比Otsu算法提高了47.2%,比1.5*R-G算法提高了11.03%;表明利用光照无关图可以有效地去除苹果表面的阴影,将其应用于阴影影响下的苹果目标的识别是可行的。
Rapid and accurate recognition of apple target with shadows on its surface is one of the key problems which must be solved for apple picking robot＇s vision system. In order to realize rapid and accurate recognition of apple target under influence of shadow, a shadow removal method based on illumination invariant image was proposed. Firstly, the red component image of original image was extracted, which can highlight the unshaded area and high brightness area of apple, and keep the shadow areas; Secondly, the illumination invariant image of original apple image was extracted. The illumination invariant image obtained highlights the shadow areas and weakens the areas of strong light, which is just opposite to red component image. Thirdly, the apple image with shadow removal could be obtained by adding the illumination invariant image to red component image, which could eliminate the shadow areas effectively. Finally, Adaptive threshold segmentation algorithm was adopted to detect the apple target from the image with shadow removal. In order to verify the validity and the accuracy of the proposed method, 20 apple images affected by shadow which were captured in the natural scene were tested. The performance of the proposed method was compared to that of Otsu method and chromatic aberration segmentation algorithm based on 1.5^＊R-G. The result showed that the segmentation result of Otsu algorithm was very poor which could only identify the unshadow areas of apple and could not identify the shadow areas; chromatic aberration segmentation algorithm based on 1.5^＊R-G was greatly influenced by light, which could not identify strong light areas and some shadow areas of image; while the result of shadow removal method of apples based on illumination invariant image was better than these two methods. The proposed method can not only identify apples affected by shadow area which was caused by illumination, but also overcome the influence of the strong illumination. The average FPR of proposed method was 17.49%, which wa
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2014年第24期168-176,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
陕西省科技统筹创新工程计划项目(2014KTCL02-15)
国家高技术研究发展计划(863计划)资助项目(2013AA10230402)
陕西省自然基金资助(2014JQ3094)
关键词
图像分析
水果
算法
苹果
阴影去除
光照无关图
OTSU
色差算法
image analysis
fruits
algorithms
apples
shadow removal
illumination invariant graph
Otsu
chromatic aberration algorithm
