Green peach recognition based on improved discriminative regional feature integration algorithm in similar background

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.