Abstract
This paper proposes a green grape target segmentation algorithm against an unstructured environment. The algorithm includes three main step: (1) the coordinates of the green grape in the images were obtained and marked of the regression box by using an improved Faster-R-CNN target detection algorithm; (2) Crop the green grapes area through the regression box to reduce the influence of complex background on the image segmentation process, extract the green grape contour based on the HSV color space transformation and edge extraction algorithm; (3) The extracted green grape contour is used as a mask and synthesized onto the original image to complete the segmentation. Ten pairs of green grape images under sunny and cloudy days were segment using the proposed method respectively. The experiments show that the recognition accuracy for unoccluded and partially occluded of green grape were 92% and 82% on sunny day. On the cloud day, the recognition accuracy for unoccluded and partially occluded were 85% and 83%, respectively. The result show the accuracy and feasibility of this method for green grapes recognition during varying illumination conditions. This research provides technical support of visual localisation technology for green grapes picking robots.
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Acknowledgments
This work was supported by grants from the National Key R&D Program of China (2018YFB1308000), National Natural Science Foundation of China (51705365) and Research Projects of Universities Guangdong Province (2018KZDXM074).
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Huang, H., Lu, Q., Luo, L., Zhou, Z., Lin, Z. (2020). A Robust Green Grape Image Segmentation Algorithm Against Varying Illumination Conditions. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_29
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DOI: https://doi.org/10.1007/978-981-15-5577-0_29
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