About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
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Symposium
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AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
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Presentation Title |
B-2: Logistics Box Recognition in Robotic Industrial De-palletizing Procedure with Systematic RGB-D Image Processing Supported by Multiple Deep Learning Method |
Author(s) |
Jonghun Yoon, Jooyeop Han, Thong Phi Nguyen, Hyunggyu Kim |
On-Site Speaker (Planned) |
Jonghun Yoon |
Abstract Scope |
In an automation system of box de-palletization utilizing robots, the quality of vision-based box recognition is considered highly depending on appearance of box containing multiple types of labels and tags. Usually, a large-scale vision dataset is required to detect on diverse complexity conditions, which also need a lot of effort to construct. This paper aims to develop a systematic image processing algorithm to remove unnecessary portion and emphasize the key features. The core of the algorithm is image transformation steps utilizing the consistent generative adversarial network (Cycle GAN) for removing main obstacles of recognition such as adhesive labels or tapes. Also, the depth map-based feature extraction is proposed to emphasize required features such as boundaries of boxes. These pre-processed images are used as inputs to Mask R-CNN for vision-based de-palletizing guideline, which was validated with 400 test cases under conditions of complex surface patterns and spatial arrangement. |