用于识别紧固件的工业强度管道

Nashlie H. Sephus, Sravan Bhagavatula, Palash Shastri, Eric Gabriel
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引用次数: 2

摘要

图像分类和计算机视觉搜索在当今的技术和消费市场中迅速兴起。具体来说,初创公司利用最先进的图像搜索功能,自动识别徽标和标题、基于视频内容的弹出式广告,以及时尚行业的产品推荐。partic专注于替换零件的图像搜索,我们展示了我们的工业管道,并应用于紧固件。我们讨论了如何克服以下问题:获取足够的训练数据,对许多不同类型的紧固件进行训练和分类,确定紧固件的定制规格(如成品类型,尺寸等),为用户建立足够好的图像约束,以及与数千个紧固件相关的许多数据块的可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Industrial-Strength Pipeline for Recognizing Fasteners
Image classification and computer vision for search are rapidly emerging in today's technology and consumer markets. Specifically, startup companies have leveraged state-of-the-art image search capabilities in automating recognition of logos and titles, pop-up advertisements based on video content, and recommendations of products in the fashion industry. Partpic focuses on image search for replacement parts, and we present our industrial pipeline for such, with application to fasteners. We discuss how we have aimed to overcome issues such as acquiring enough training data, training and classification of many different types of fasteners, identification of customized specifications of fasteners (such as finish type, dimensions, etc.), establishing constraints for the user to take an good-enough image, and scalability of many pieces of data associated with thousands of fasteners.
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