中文摘要：

针对目前野外环境下多类目标识别速度偏慢,导致机器人视觉定位精度低和工作效率不高的难题,以野外环境下成熟荔枝的多类色彩目标识别为例,提出了一种双次Otsu分割算法对多类色彩目标进行识别。首先为了提高算法的效率,改进了传统的Otsu算式;然后对目标色彩图像的背景、果梗、果实分别用改进的Otsu算法进行粗分割和细分割。最后通过与K-均值聚类（K-means）算法、模糊C均值聚类（FCM）算法、Otsu和K-means结合算法、Otsu和FCM结合算法这4种算法进行对比,双次Otsu算法从分割质量及其正确分割率、运行时间、稳定性3方面都优于其他4种算法。实验结果表明,双次Otsu算法对色彩目标的成熟荔枝识别的时间少于0.2 s。

英文摘要：

An object in the field environment usually contains two or more classes of color targets.Fast recognition of color target image is the key technology for robot positioning and operation,which is widely used in the field of military,natural disaster rescue,agricultural harvesting robot,etc.However the speed of multi-target recognition in the field environment is usually slow,which makes the visual positioning precision of robots lower at present.This paper proposed a double Otsu segmentation method based on the improved Otsu algorithm for the recognition of multiple targets.To prove the effectiveness of this method,it was used on mature litchi recognition in the field environment.First of all,in order to improve the efficiency,the traditional Otsu algorithm was improved.Then the background,stem and fruit of the target color image were respectively recognized by using the improved Otsu algorithm.Compared with the K-means clustering（K-means） algorithm,the fuzzy C-mean clustering（FCM） algorithm,the Otsu and K-means algorithm,and the Otsu and FCM algorithm,the double Otsu segmentation algorithm was superior to the other four algorithms on the segmentation quality and correctness rate,the running time and stability.The test results showed that the recognition time for the mature litchi by using the double Otsu segmentation algorithm was less than 0.2 s.The effectiveness of the algorithm was verified through the experiment.