| 基于改进判别区域特征融合算法的近色背景绿色桃子识别 |
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| 引用本文： | 黄小玉,李光林,马驰,杨士航.基于改进判别区域特征融合算法的近色背景绿色桃子识别[J].农业工程学报,2018,34(23):142-148. |
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| 作者姓名： | 黄小玉 李光林 马驰 杨士航 |
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| 作者单位： | 西南大学工程技术学院，重庆 400715,西南大学工程技术学院，重庆 400715,西南大学工程技术学院，重庆 400715,西南大学工程技术学院，重庆 400715 |
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| 基金项目： | 重庆市科委重点项目（csk2016shmszx80018）；重庆市研究生科研创新项目（CYS18109） |
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| 摘 要： | 针对机器视觉识别中自然光照条件下未成熟绿色果实的识别存在颜色与背景相似、光照不均、果叶遮挡等问题,该文提出在判别区域特征集成(discriminative regional feature integration,DRFI)算法框架的基础上,结合颜色、纹理、形状特征,对未成熟绿色桃子进行识别。首先通过基于图的图像分割(graph-based image segmentation)算法,取不同的参数将图像分割为多层,再计算各层图像的显著图,并用线性组合器将其融合,得到DRFI显著图。再用OTSU算法得到的阈值自适应调整之后对DRFI显著图进行分割,减少了显著图中识别为低概率果实的误分割。对于分割后仍存在的果实相互粘连的情况,通过控制标记符和距离变换相结合的分水岭分割算法将其分开。试验结果表明:该方法在训练集中的准确识别率为91.7%,在验证集中的准确识别率为88.3%,与相关文献报道的结果以及原始DRFI算法在验证集中的检测结果相比,该文方法的准确识别率提高了3.7~10.7个百分点,较有效地解决了颜色相近和果叶遮挡问题,可为果树早期估产和绿色果实采摘自动化、智能化提供参考。
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| 关 键 词： | 机器视觉 图像处理 算法 桃子 显著对象检测 特征提取 分水岭变换 识别 |
| 收稿时间： | 2018/7/31 0:00:00 |
| 修稿时间： | 2018/10/26 0:00:00 |
Green peach recognition based on improved discriminative regional feature integration algorithm in similar background |
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| Huang Xiaoyu,Li Guanglin,Ma Chi and Yang Shihang.Green peach recognition based on improved discriminative regional feature integration algorithm in similar background[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(23):142-148. |
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| Authors: | Huang Xiaoyu Li Guanglin Ma Chi and Yang Shihang |
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| Institution: | College of Engineering and Technology, Southwest University, Chongqing 400715, China,College of Engineering and Technology, Southwest University, Chongqing 400715, China,College of Engineering and Technology, Southwest University, Chongqing 400715, China and College of Engineering and Technology, Southwest University, Chongqing 400715, China |
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| Abstract: | Abstract: In order to solve the problems in the recognition of immature green fruits under natural illumination in machine vision recognition, such as the color similarity between the fruits and the background, uneven illumination and partial occlusion, etc., in this paper, color, texture and shape features of green peach were combined to identify immature green peach based on the DRFI (discriminative regional feature integration) algorithm. The color features included the mean of R component minus B component, Hue component. The texture features were variances of LM(Leung-Malik) filter bank response and LBP(local binary pattern) feature, and the shape features included area, perimeter, circularity, major axis length, minor axis length, length-width ratio, major-axis length to perimeter ratio and eccentricity. The DRFI algorithm mainly had 3 steps, that was, the multi-level segmentation, saliency computation in each level and multi-level saliency fusion. Firstly, the input image was preprocessed based on the multi-level segmentation, which were generated in the graph-based image segmentation algorithm with different control parameters of standard deviation of kernel function of the Gaussian filter (sigma), the number of the merged region (k), and the minimal pixels of segmented region (min). With the values of control parameters changing, different image segmentation results were obtained. In this paper, the input image was divided into 25 layers in the training set and each layer was further divided into several super-pixels. Secondly, the super-pixel in each layer had 26 feature variables, which included 2 color features, 16 textural features and 8 shape features. The segmentation results of each layer of the input image were matched with the ground truth map, then the tag of the super-pixel was produced, which was the positive one (the peach) or the negative one (the background). The 26 dimensional feature vector and tag of each super pixel were inputted into the random forest model, and the regression model was trained, and then the saliency map of each layer segmentation image was calculated by the model. Thirdly, the DRFI saliency map was obtained by a linear combiner to fuse the multi-level saliency map , whose weights was given through a least square estimator. To effectively detect the immature green peach in natural environment, the DRFI saliency map needed to be processed further. So adaptive segmentation threshold from the OTSU algorithm for DRFI saliency map must be adjusted to reduce the wrongly segmentation of the fruit with low probability in the saliency map. Mathematical morphology was then used, such as removing noise from the binary map. The watershed segmentation algorithm which combined the maker-controlled and distance transform was used to separate the fruit which still existed adhesion after segmentation. A total of 186 images were collected as the samples for experiment. 150 images were randomly selected as the training set, and the remaining 36 images were as the validation set. The experimental results of peach images recognition showed that the recognition accuracy of the proposed method in this paper in the training set was 91.7%, and the accuracy in the validation set reached 88.3%. At the same time, the recognition results of the proposed method outperformed the results from other methods, including Kurtulmus et al.(2014), Ma et al.(2016), and original DRFI algorithm(2017). Furthermore, the proposed algorithm could show a good performance in the complex scenes such as sunny side, shadow side, occlusion and overlap. The recognition results revealed that the proposed method could provide reference for early estimation of fruit yield and picking of green fruit automatically and intelligently. |
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| Keywords: | machine vision image processing algorithms peach salient object detection feature extraction watershed transform recognition |
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