Object location estimation system based on instance segmentation
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초록
Owing to the increasing demand for parcel delivery, automated systems for the reception or organization of parcels in warehouses have been widely researched. Application of existing automation systems has been challenging owing to the high con-struction costs incurred and limited lighting conditions required. To address these issues, this paper proposes a system that can deter-mine the location and area of a measured object by using instance segmentation. The proposed system uses YOLACT, a lightweight image-segmentation algorithm optimized in real time, to determine the pixel-level area of the target in the image. The proposed system can estimate the location and area of an object in an image, calculate the pixel area, and accurately identify non-square objects. Fur-thermore, the proposed system can accurately estimate the position of an object even when the lighting changes by directly constructing and learning datasets collected in various lighting environments.
Abstract
Owing to the increasing demand for parcel delivery, automated systems for the reception or organization of parcels in warehouses have been widely researched. Application of existing automation systems has been challenging owing to the high con-struction costs incurred and limited lighting conditions required. To address these issues, this paper proposes a system that can deter-mine the location and area of a measured object by using instance segmentation. The proposed system uses YOLACT, a lightweight image-segmentation algorithm optimized in real time, to determine the pixel-level area of the target in the image. The proposed system can estimate the location and area of an object in an image, calculate the pixel area, and accurately identify non-square objects. Fur-thermore, the proposed system can accurately estimate the position of an object even when the lighting changes by directly constructing and learning datasets collected in various lighting environments.
참고문헌 (31)
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