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Application of Amodal Segmentation on Cucumber Segmentation and Occlusion Recovery by Sungjay Kim; Suk-Ju Hong; Jiwon Ryu; Eungchan Kim; Chang-Hyup Lee; Ghiseok Kim is a Agricultural and Biological Sciences article available to read on EtoBox.

What is Application of Amodal Segmentation on Cucumber Segmentation and Occlusion Recovery about?

Computer vision tasks, such as image recognition, object detection, and semantic segmentation, have contributed tremendously to autonomous harvesting. However, these tasks are applicable to the visible parts of an object in an image. In the field of agriculture, detecting green vegetables with subtle color differences poses an additional challenge. The nature of cucumbers (Cucumis sativus) makes them difficult to detect. To address this issue, we reconstructed the occluded part of the cucumber to help autonomous robots detect and locate picking-point positions. A dataset of cucumber images from two farms located in South Korea was generated. The dataset was superimposed with synthetic leaf patches to simulate the effect of occlusion. Using this dataset, we employed amodal segmentation with an auto-encoder and an ablation study regarding shape prior post-process, shape prior refinement, and feature matching to determine a suitable method for our cucumber dataset. We then proposed amodal segmentation with a U-net reconstruction network as a novel model for cucumber occlusion recovery. In the ablation study, the model with no additional process showed the highest accuracy, average pre

Who reads Application of Amodal Segmentation on Cucumber Segmentation and Occlusion Recovery?

Author

Sungjay Kim; Suk-Ju Hong; Jiwon Ryu; Eungchan Kim; Chang-Hyup Lee; Ghiseok Kim

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Agricultural and Biological Sciences (Physical Sciences)