Automatic Anomaly Mark Detection on Fabric Production Video by Artificial Intelligence Techniques
學年 110
學期 2
發表日期 2022-07-22
作品名稱 Automatic Anomaly Mark Detection on Fabric Production Video by Artificial Intelligence Techniques
作品名稱（其他語言）
著者 Nantachaporn Rueangsuwan; Nathapat Jariyapongsgul; Chen, Chien-chang; Lin, Cheng-shian; Somchoke Ruengittinun; Chalothon Chootong
作品所屬單位
出版者
會議名稱 5th IEEE International Conference on Knowledge Innovation and Invention 2022 (IEEE ICKII 2022)
會議地點 Hualien, Taiwan
摘要 In the previous era, humans played important roles in all aspects of industrial work. However, they indisputably made many errors that can be mitigated by automated manufacturing, thus revealing the importance of the latter. In this paper, an autoencoder-based fabric-defect detection method via video is presented. The fabric-production video is segmented using frames to produce images, and then a VGG16-based autoencoder is applied to reconstruct the original image. In the proposed scheme, each fabric-production image is normalized to 256 x 256 pixels, which provided excellent results compared with using various margin sizes in our experiments. We used the structural similarity index (SSIM), which measures similarity when checking whether image regions are normal or defective. Moreover, a masking algorithm is utilized to improve detection accuracy. Based on our experiments, we found that 0.5 is an appropriate value for setting the SSIM threshold as it produced the best detection performance with a defect detection accuracy of ~99%.
關鍵字 Training;Knowledge engineering;Technological innovation;Image segmentation;Production;Fabrics;Manufacturing
語言 en_US
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20220722~20220724
通訊作者
國別 USA
公開徵稿
出版型式
出處 2022 IEEE 5th International Conference on Knowledge Innovation and Invention (ICKII )
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125336 )