基于机器视觉的番茄多目标提取与匹配

果实的提取和匹配是番茄采摘机器人进行番茄定位和采摘的基础。为解决获取图像中多个成熟番茄粘连或被遮挡的情况下果实的提取和匹配问题,该文提出了使用局部极大值法和随机圆环变换检测圆算法结合进行目标提取,再使用SURF算法进行目标匹配的算法。该方法首先基于颜色对番茄进行分割提取,然后使用局部极大值法对番茄个数进行估计,结合番茄区域面积进行半径估计,之后通过随机圆环变换算法检测番茄中心和半径进行目标定位,再使用SURF算法进行双目目标匹配的算法。该方法在一定程度上解决了复杂自然环境下,多个番茄的提取和图像特征匹配的问题,并通过试验验证了其有效性和准确性,可为采摘机器人目标识别技术的研究提供参考。

Tomato targets extraction and matching based on computer vision

Tomato targets extraction and matching is the basis for tomato location and picking of tomato harvesting robot. It is a challenge to match the targets extracted from images captured by different cameras, when the number of the tomatoes is more than three, especially with the presence of clutter and occlusion. A novel tomato targets extraction and matching algorithm is proposed in this paper to solve this problem. Firstly, the image segmentation algorithm is developed to segment the targets from the background, for the tomato images captured based on color analysis. Secondly, a method for slip touching tomatoes separation based on gray-scale local maxima is used to estimate the number and radius of the tomatoes based on the extracted tomato area. Then, RCR (Random Circle Ring) method is applied to extract the center and radius of the tomatoes. The last but not the least, tomato targets extracted from the image captured by the first camera are matched with the ones in the image captured by the second camera through applying SURF (speeded up robust features) algorithm. Experimental results showed that the approach proposed in this paper solved the extraction and matching of tomato targets with the presence of clutter and occlusion to some extent, while achieving obvious validity and accuracy.