马翠花, 张学平, 李育涛, 林 松, 肖德琴, 张连宽. 基于显著性检测与改进Hough变换方法识别未成熟番茄[J]. 农业工程学报, 2016, 32(14): 219-226. DOI: 10.11975/j.issn.1002-6819.2016.14.029
    引用本文: 马翠花, 张学平, 李育涛, 林 松, 肖德琴, 张连宽. 基于显著性检测与改进Hough变换方法识别未成熟番茄[J]. 农业工程学报, 2016, 32(14): 219-226. DOI: 10.11975/j.issn.1002-6819.2016.14.029
    Ma Cuihua, Zhang Xueping, Li Yutao, Lin Song, Xiao Deqin, Zhang Liankuan. Identification of immature tomatoes base on salient region detection and improved Hough transform method[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(14): 219-226. DOI: 10.11975/j.issn.1002-6819.2016.14.029
    Citation: Ma Cuihua, Zhang Xueping, Li Yutao, Lin Song, Xiao Deqin, Zhang Liankuan. Identification of immature tomatoes base on salient region detection and improved Hough transform method[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(14): 219-226. DOI: 10.11975/j.issn.1002-6819.2016.14.029

    基于显著性检测与改进Hough变换方法识别未成熟番茄

    Identification of immature tomatoes base on salient region detection and improved Hough transform method

    • 摘要: 通过自动识别自然环境下获取果实图像中的未成熟果实,以实现自动化果实估产的目的。该文以番茄为对象,根据视觉显著性的特点,提出了使用基于密集和稀疏重构(dense and sparse reconstruction,DSR)的显著性检测方法检测未成熟番茄果实图像,该方法首先计算密集和稀疏重构误差;其次使用基于上下文的重构误差传播机制平滑重构误差和提亮显著性区域;再通过多尺度重构误差融合与偏目标高斯细化;最后通过贝叶斯算法融合显著图得到DSR显著灰度图。番茄DSR灰度图再经过OTSU算法进行分割和去噪处理,最终使用该文提出的改进随机Hough变换(randomized hough transform,RHT)圆检测方法识别番茄果簇中的单果。结果显示,该文方法对未成熟番茄果实的正确识别率能达到77.6%。同时,该文方法与人工测量的圆心和半径的相关系数也分别达到0.98和0.76,研究结果为估产机器人的多种果实自动化识别提供参考。

       

      Abstract: Abstract: The identification of fruit crop image plays an important role in the automatical estimation of production. However, occlusion, varying illumination, and similarity with the background make fruit identification become a very challenging task. Green tomato detection with green canopy is a very difficult problem. In this paper, we first put forward the dense and sparse reconstruction (DSR) method to detect immature fruit images. This method first computes dense and sparse reconstruction errors. Second, the reconstruction errors are propagated based on the contexts obtained from K-means clustering. Third, pixel-level saliency is computed by an integration of multi-scale reconstruction errors and refined by an object-biased Gaussian model. Finally this method applies the Bayes formula to integrate saliency measures based on dense and sparse reconstruction errors. The DSR detection images of tomato are obtained by the computation mentioned previously. Then the OTSU method is used to segment the DSR detection images, and the opening operation is used for removing the noise area after the split. In particular, in order to identify single fruit out of tomato fruit clusters, the circular Hough transformation (CHT) or randomized Hough transform (RHT) can be used, but they have some shortages (e.g. a large amount of calculation and space overhead) when recognizing tomato fruit. So we trade off the advantages and disadvantages of CHT and RHT, and propose an improved randomized Hough transform (IRHT) circle detection method. First, we adopt the boundary tracking algorithm to obtain tomato image boundary after segmentation. Secondly, in order to obtain circles and radii, we utilize the subsection and interval point group selection method to improve the accuracy of identification and reduce redundant computation. However, some of circles and radius may be in the same circle, or may be too big or small, so we need to find the real circles. Finally, in order to eliminate repetitive circles to get the last circle target (the fruit), we compute the Euclidean distance of center coordinates of 2 circles, and accumulate those circles who are in the same real circle to generate actual circle (a tomato fruit). In this paper, we compare the test results of 3 methods, namely the traditional CHT, RHT method and our proposed method. And we find that the correct rate of our proposed improved algorithm of immature image recognition can reach 77.6%. Moreover, the correlation coefficients of circle centers and radii of tomato fruit between ground truth and calculated value by our method are 0.98 and 0.76, respectively. The average relative error of center coordinates of circle is 7.6%, and the average relative error of radii of circle is 14.0%. The confidence level of mean in the confidence interval (42.03, 49.48) is 95%, and the confidence level of variance in confidence interval (10.25, 15.64) is 95%. Based on the results of our study, we find that the universal applicability of our method is stronger, in addition, our method is not only suitable for tomatoes, but also applicable to other kinds of cone crops. Therefore, our method lays a solid foundation to achieve the goal of production estimates of a variety of fruits by robots.

       

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