基于两级分割与区域标记梯度Hough圆变换的苹果识别

自然环境下果实的准确分割与快速识别是采摘机器人作业面临的难题之一。针对自然环境中的成熟苹果,该研究提出一种基于Otsu与分水岭相结合的两级分割算法与区域标记梯度Hough圆变换的苹果识别方法。首先,使用亮度自适应校正算法对表面亮度分布不均的苹果图像进行校正,增强图像的细节信息。结合果实颜色特征,提取YCbCr颜色空间的Cr分量图像作为预处理样本。然后,采用改进后的Otsu算法进行初次分割,得到苹果目标的二值图像,该算法通过引入形态学开-闭重建滤波去除大量背景噪声,通过缩减灰度级遍历范围提高分割速率。采用基于距离变换的分水岭算法进行二次分割,分离粘连果实区域,提取目标苹果的外部轮廓。最后,在轮廓外设置最小外接矩形标记有效区域,在标记区域内进行梯度Hough圆变换实现苹果目标的自动识别。对自然环境中采集的200幅苹果图像进行测试,并与传统梯度Hough圆变换方法进行对比,本文方法在顺、逆光下的识别准确率为90.75和89.79%,比传统方法提高了15.03和16.41%,平均识别时间为0.665和0.693 s,比传统方法缩短了0.664和0.643 s。所提的两级分割算法不仅可以从复杂环境中准确分割果实目标区域,而且可以从粘连果实区域中提取单个果实边界。利用区域标记的梯度Hough圆变换方法能够快速准确地对果实进行识别。研究结果能满足苹果采摘机器人对不同光照下目标识别速度和精度的要求,可为苹果等类球形果实的快速识别提供参考。

Apple recognition based on two-level segmentation and region-marked gradient Hough circle transform

Apples are produced in the large quantities each year, particularly for as the largest economic fruit in China. It is highly required for the rapid picking within the harvesting period. Therefore, the automatic apple picking is essential to the apple harvesting in intensive farming. An accurate and rapid identification of fruit can be fundamental for the automatic picking. However, some environmental factors surrounding the fruit can pose a great interference in the fruit identification under the natural, complex, and variable backgrounds, such as the light intensity, occlusion, and overlap of the fruit. In this study, an apple recognition was proposed using two-level segmentation and region-marked Hough transform. Experimental results show that the robust and practical performance was achieved for the apple recognition under different illumination, branch and leaf occlusion, as well as the fruit overlap. Specific steps were as follows. Firstly, the front camera (NikonD90) of the information acquisition robot was used to capture from 600-800 mm away from the fruit tree under the conditions of nature natural light and backlight, respectively. The brightness adaptive correction algorithm was then used to correct the brightness of apple images with the uneven distribution of surface brightness, in order to enhance the image details. The Cr component images of YCbCr color space were extracted as the preprocessing samples to combine with the feature of the color of the apple. Secondly, the improved Otsu algorithm was utilized to obtain the binary image of the apple target for the initial segmentation, in order to accurately extract the contour of the target fruit under different growth states (mainly including single and double fruits with the overlap and occlusion). A morphological open-close reconstruction filter was also introduced to the Otsu algorithm to remove the background noise. The traversal range of the gray level was reduced to shorten the complexity and running time of the algorithm for the high segmentation rate. Thirdly, the watershed algorithm was combined to perform the secondary segmentation of the segmented fruit region using distance transformation. The conglutinated and overlapping apples were separated to effectively extract the apple target contour. Finally, the gradient Hough circle transformation was selected to identify the number of apples. But the algorithm traveled through the whole image for the computational complexity, time time-consuming, and easy to produce the false identification. Therefore, the minimum circumscribed rectangle outside the contour was set as the effective area for the gradient Hough circle transform in the effective area, particularly for the recognition speed and accuracy. The experimental results show that: 1) The improved Otsu algorithm was achieved in the higher segmentation accuracy of fruit targets, especially with the less segmentation time. The improved algorithm was also filled the tiny holes in the apple to suppress the noise in the background branches and leaves, further to more clearly segment the target region more than before. The average segmentation time was 1.458 s, which was 0.643 and 1.060 s shorter than the Otsu and K-means algorithm before the improvement. In the adhesion of partial apple regions in the binary images, a watershed segmentation with the distance transformation was used for the quadratic segmentation to effectively separate the sticky apples for the full apple target boundary. 2) The gradient Hough circle transform was used to recognize the 200 apple images under different lighting conditions, where the recognition accuracy was 90.75% in the nature natural light and 89.79% in the backlight, which were improved by 15.03% and 16.41%, respectively, compared with the traditional. The average recognition time was 0.665 and 0.693 s, which was 0.664 and 0.643 s shorter than before. Therefore, the proposed algorithm can meet the requirements of apple-picking robots, in terms of recognition speed and accuracy. The findings can provide a strong reference for the fast recognition of spherical fruits, such as apples.