弱光复杂背景下基于MSER和HCA的树上绿色柑橘检测

基于图像处理和机器视觉的树上绿色柑橘检测,能为果园管理者施肥、估产及采摘作业提供指导.该文提出一种基于水果表面光照分布的分层轮廓分析(hierarchical Contour Analysis,HCA)算法实现了树上绿色柑橘的检测.彩色数码相机拍摄弱光下由闪光灯补光的树上柑橘场景彩色图像,基于水果表面的光照分布应用最大稳定极值区域(maximally stable extremal region,MSER)算法提取图像中的感兴趣区域,然后建立感兴趣区域周围的分层轮廓图,并利用霍夫变换拟合每一级轮廓获得分层圆形目标,最后进行拟合圆嵌套分析得到绿色柑橘水果目标.所提算法在20张复杂的柑橘果园场景图像中进行了测试,最终的召回率达81.2%,查准率达到83.5%,单幅图像平均处理时间为3.70 s.该文所提出的基于光照分布的分层轮廓分析算法,不仅适用于绿色柑橘的检测,也可为其他树上绿色水果检测提供通用的框架和思路.

Detecting green citrus fruit on trees in low light and complex background based on MSER and HCA

Abstract: Accurate crop-load estimation is very important for efficient management of nutrients and harvest operations. Current machine vision techniques for crop-load estimation have achieved only limited success mostly due to partial occlusion, shape irregularity, varying illumination and multiple sizes. Detecting immature green fruit is a more challenging task for similar color of fruit and background. The key starting point of this paper for detecting immature citrus fruit was the observation that the light distribution on citrus fruit follows a general pattern in which the light intensity decreases with the distance from a local maximum due to specular reflection. Immature citrus fruit detection was achieved by detecting this pattern with concentric circles or parts of circles. This pattern was proposed with the maximally stable extremal region (MSER) method and validated by hierarchical contour analysis (HCA) which was the first proposed in this paper. The images were captured by a color camera under low natural light conditions with a flashlight, and the green component of the color images was used for further analysis. After smoothing the whole image by Weiner filter, the regions of interest (ROIs) in the image were extracted by the method of MSER. The ROIs detected by MSER were those whose support was nearly the same over a range of thresholds, so the regions on citrus fruit were detected by MSER for the pattern that the light intensity decreases stably and gently with the distance from a local maximum. However, many regions on leaves and background were also detected as ROIs and should be excluded in the next step. A novel algorithmic technique was proposed to remove these regions on background, and this method was named as the HCA. Firstly, shape analysis was used for each ROI and only those ROIs were considered as valid if the shape was nearly circular. Secondly, multiple levels of contours around each valid ROI were extracted and fitted with the circular Hough transform (CHT). Lastly, multiple fitted circles would be merged into one if their most parts were overlapped together, this step was called circle merging and the merged circles were considered as the last detected citrus fruits. The algorithm was tested on a testing dataset with 20 images and achieved the recall rate of 81.2% and the precision rate of 83.5%. The processing time of the proposed method was 3.70 s totally on each image, on average, in which 0.57 s was used for MSER detection and 3.13 s was used for HCA. The result showed that the proposed method can detect green citrus fruit in a very difficult and challenging scene with so many fruits in one image and extensive partial occlusion. The good performance of partial occlusion tolerance of the proposed method in this paper is mainly due to that the proposed HCA doesn''t use the shape of outer contour of fruit, but uses multiple concentric contours which come from the pattern of light intensity distribution on fruit surface. The research framework in this paper can give a novel thought on other green fruit detection besides citrus fruit.