fieldid E-Thesis & Research สถาบันเทคโนโลยีไทย-ญี่ปุ่น
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บทคัดย่อ
OBJECT DETECTION FOR RETAIL PRODUCT RECOGNITION : The retail sector is a vital driver of economic expansion. The retail industry must embrace technological advancements to augment productivity, streamline operations, and minimize human errors to uphold its crucial economic role. During the COVID-19 pandemic, the purchasing power volumes globally grew from February 2020 to April 2021, and the retail sector gained 35 percent in market capitalization. This growth underscores a compelling research opportunity in the retail industry. Consequently, artificial intelligence (AI) has become a focus of significant interest and has widely adopted technology, which includes computer vision to recognize and detect retail products. This study explores the efficacy of YOLO (You Only Look Once) in retail product recognition. The research compares different YOLO versions and evaluates their ability to identify on-shelf grocery items. In this research, YOLOv8 is applied and divided into five subcategories: YOLOv8n (nano), YOLOv8s (small), YOLOv8m (medium), YOLOv8l (large), and YOLOv8x (extra-large). The models are assessed utilizing Grozi-120 and SKU110K datasets. The evaluation metrics are precision, recall, mAP50, and mAP50-95. The result demonstrates that YOLOv8x yields the best overall performance across both datasets. Remarkably, a mAP50 score of 92.6 percent is achieved on the SKU110K. In conclusion, the outcomes indicate that the YOLOv8 model works well for retail product detection. The model efficacy shows potential for effectiv
ผู้แต่ง
Nakul Pannoy
ประเภทสิ่งพิมพ์
Thesis (MIT) 2024
เลขหน้า
68
หัวเรื่อง
Information Technology--Thesis
หัวเรื่อง
Thai-Nichi Institute of Technology.Information Technology--Thesis
เอกสารฉบับเต็ม
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