CAJ | 학술논문

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。
음영영향하평과목표적쾌속준학식별시평과채적궤기인시각계통필수해결적관건기술지일。위료실현음영영향하평과목표적준학식별，해연구채용광조무관도이론실현료평과표면음영적거제。이자연장경하획취적수불동정도음영영향적평과목표도상위연구대상，수선이용광조무관도원리획취음영평과도상적광조무관도，체도돌출평과목표음영구역적목적；기차제취원도상적홍색분량신식병여관조무관도진행상가처리；최후장상가후적도상진행자괄응역치분할처리，체도거제음영적목적。위료험증해산법적유효성여준학성，이용20폭수음영영향적평과목표도상진행료시험，병여Otsu산법、1.5*R-G색차산법진행료대비，시험결과표명：Otsu산법부능식별출미수음영영향적평과구역；1.5*R-G 색차산법수광조영향교대，대우평과도상적상대강광조구역화부분음영구역불능유효식별；기우광조무관도적평과표면음영거제방법대음영영향하적평과목표도상분할효과교호，가이극복광조과강적문제，병준학식별출음영영향하적평과목표。문중산법적평균가양성솔위17.49%，비Otsu산법강저료52.84%，비1.5*R-G산법강저료26.18%；문중산법적평균중첩계수위86.59%，비Otsu산법제고료47.2%，비1.5*R-G산법제고료11.03%；표명이용광조무관도가이유효지거제평과표면적음영，장기응용우음영영향하적평과목표적식별시가행적。
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 was decreased by 52.84% and 26.18%respectively, compared to Otsu algorithm and chromatic aberration algorithm based on 1.5*R-G. The average OI was 86.59%, which was increased by 47.2%and 11.03%, compared to Otsu algorithm and chromatic aberration algorithm based on 1.5*R-G. Thus, it could be concluded that apple images under influence of shadow can be effectively identified by the proposed method in this paper, which is feasible in identifying the apple target with shadow on its surface.