基于深度学习的PET瓶坯划痕检测

2020 2nd International Conference on Information Technology and Computer Application (ITCA) Pub Date : 2020-12-01 DOI:10.1109/ITCA52113.2020.00054

Lei Zhou, Jianan Liang, Yongjun Cao

{"title":"基于深度学习的PET瓶坯划痕检测","authors":"Lei Zhou, Jianan Liang, Yongjun Cao","doi":"10.1109/ITCA52113.2020.00054","DOIUrl":null,"url":null,"abstract":"Scratches are a common phenomenon in the production of the PET bottle preform, and traditional inspection by human eyes bring troubles to the automatic production process. In this paper, deep learning algorithm was used to detect PET bottle preform scratches. The detection algorithm based on YOLOv4 neural network was proposed. The detection effect was tested under different scratch degrees, which provide accurate information for automatic production picking of the PET bottle preform. The average detection time of a single image was 0.154s, and the shortest detection time was 0.133s.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"25 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Scratch Detection of the PET Bottle Preform Based on Deep Learning\",\"authors\":\"Lei Zhou, Jianan Liang, Yongjun Cao\",\"doi\":\"10.1109/ITCA52113.2020.00054\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Scratches are a common phenomenon in the production of the PET bottle preform, and traditional inspection by human eyes bring troubles to the automatic production process. In this paper, deep learning algorithm was used to detect PET bottle preform scratches. The detection algorithm based on YOLOv4 neural network was proposed. The detection effect was tested under different scratch degrees, which provide accurate information for automatic production picking of the PET bottle preform. The average detection time of a single image was 0.154s, and the shortest detection time was 0.133s.\",\"PeriodicalId\":103309,\"journal\":{\"name\":\"2020 2nd International Conference on Information Technology and Computer Application (ITCA)\",\"volume\":\"25 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 2nd International Conference on Information Technology and Computer Application (ITCA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITCA52113.2020.00054\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITCA52113.2020.00054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

划伤是PET瓶坯生产中常见的现象，传统的人眼检测给自动化生产过程带来麻烦。本文采用深度学习算法检测PET瓶坯划痕。提出了基于YOLOv4神经网络的检测算法。测试了不同划伤程度下的检测效果，为PET瓶坯的自动生产拾取提供了准确的信息。单幅图像的平均检测时间为0.154s，最短检测时间为0.133s。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Scratch Detection of the PET Bottle Preform Based on Deep Learning

Scratches are a common phenomenon in the production of the PET bottle preform, and traditional inspection by human eyes bring troubles to the automatic production process. In this paper, deep learning algorithm was used to detect PET bottle preform scratches. The detection algorithm based on YOLOv4 neural network was proposed. The detection effect was tested under different scratch degrees, which provide accurate information for automatic production picking of the PET bottle preform. The average detection time of a single image was 0.154s, and the shortest detection time was 0.133s.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2020 2nd International Conference on Information Technology and Computer Application (ITCA)

自引率

0.00%

发文量