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Abstract The immature citrus fruits shared similar colour information with the other background participants under natural orchard environment and might encounter the phenomena of object adhesion. To this end, an improved GB chromatic mapping algorithm was presented to filter out the background regions from the input RGB images as many as possible. Based on the remaining regions, the foreground and local background regions were re-constructed using mathematical morphology operations, respectively, and the foreground and background markers were labelled from the re-constructed foreground and background images, respectively; the potential fruit regions of adhesion/overlap were further segmented using markers-controlled watershed transform; then, the texture and shape feature were extracted from those potential regions using local binary pattern (LBP) and histogram of oriented gradients (HOG), respectively. Finally, based on the extracted dual modality features, a logic “and” operation fusion strategy by combining the decision results provided by support vector machines (SVM) was presented to locate the citrus fruits and further improve the reliability of the detection methodology. Experimental results on the validation dataset demonstrated that the detection accuracy and statistical indices F1-measure reached 0.81 and 0.89, respectively, with the average false detections of only 0.02 per image, indicating that the presented method could offer a reference for the yield estimation before citrus fruits maturation stage.
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Received: 08 January 2021
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2020, Vol. 33 
