Optimal YOLO-based Model for Fabric Anomaly Detection
學年 113
學期 1
發表日期 2025-01-19
作品名稱 Optimal YOLO-based Model for Fabric Anomaly Detection
作品名稱（其他語言）
著者 Nonpawit Silabumrungrad; Supakrit Somridjinda; Chien-Chang Chen; Jirawan Charoensuk; Chalothon Chootong
作品所屬單位
出版者
會議名稱 Ubi-Media 2025/ I-SPAN 2025
會議地點 Bangkok, Thailand
摘要 Fabric anomaly detection is a crucial application in the industry. This study identifies the optimal YOLO (You Only Look Once) algorithm from a selection of YOLO versions for detecting fabric anomalies, including defect identification and region localization. Recent YOLO models, including YOLOv5, YOLOv7, YOLOv8, and YOLOv9, are evaluated with batch sizes of 4, 8, and 16. Additionally, computation times for detection are compared. The dataset is generated from numerous images extracted from a fabric video, with test images categorized as normal, line defect, or hole defect. Experimental results show that YOLOv9 batch 4 achieves the highest F1-score for defect detection, while YOLOv8 batch 16 offers a balance of optimal mAP and reduced training time. Larger batch sizes consistently enhance training efficiency across all models. Further experiments can extend this approach to other fabric datasets to detect various types of defects.
關鍵字
語言 zh_TW
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20250119~20250123
通訊作者
國別 TWN
公開徵稿
出版型式
出處
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129059 )