Název:	Training Image Synthesis for Shelf Item Detection reflecting Alignments of Items in Real Image Dataset
Autoři:	Tomokazu, Kaneko Sakai, Ryosuke Shiraishi, Soma
Citace zdrojového dokumentu:	WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 81-89.
Datum vydání:	2023
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/54413
ISBN:	978-80-86943-32-9
ISSN:	2464–4617 (print) 2464–4625 (CD/DVD)
Klíčová slova:	detekce objektu;syntéza tréninkových dat;rozpoznávání maloobchodních položek;automatická anotace
Klíčová slova v dalším jazyce:	object detection;training data synthesis;retail item recognition;automatic annotation
Abstrakt v dalším jazyce:	We propose a novel cut-and-paste approach to synthesize a training dataset for shelf item detection, reflecting the alignments of items in the real image dataset. The conventional cut-and-paste approach synthesizes large numbers of training images by pasting foregrounds on background images and is effective for training object detection. However, the previous method pastes foregrounds on random positions of the background, so the alignment of items on shelves is not reflected, and unrealistic images are generated. Generating realistic images that reflect actual positional relationships between items is necessary for efficient learning of item detection. The proposed method determines the pasting positions for the foreground images by referring to the alignment of the items in the real image dataset, so it can generate more realistic images that reflect the alignment of the real-world items. Since our method can synthesize more realistic images, the trained models can perform better.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2023: Full Papers Proceedings

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.