Cardboard packaging minimization: Autonomous box selection by RGB-depth vision system and complementary deep learning classification

IF 1.3 4区工程技术 Q4 CHEMISTRY, ANALYTICAL

Instrumentation Science & Technology Pub Date : 2024-07-22 DOI:10.1080/10739149.2024.2379054

Jimmy Kwon, Sungmin Kwon

引用次数: 0

Abstract

Amidst the dramatic expansion of online shopping, wasted cardboard packaging has become a significant challenge in modern-day society. In this paper, we propose an innovative approach to address th...

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纸板包装最小化：通过 RGB 深度视觉系统和深度学习分类互补实现自主选箱

随着网上购物的急剧扩张，纸板包装的浪费已成为现代社会的一个重大挑战。在本文中，我们提出了一种创新方法来解决这一问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Instrumentation Science & Technology 工程技术-分析化学

CiteScore

3.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Instrumentation Science & Technology is an internationally acclaimed forum for fast publication of critical, peer reviewed manuscripts dealing with innovative instrument design and applications in chemistry, physics biotechnology and environmental science. Particular attention is given to state-of-the-art developments and their rapid communication to the scientific community. Emphasis is on modern instrumental concepts, though not exclusively, including detectors, sensors, data acquisition and processing, instrument control, chromatography, electrochemistry, spectroscopy of all types, electrophoresis, radiometry, relaxation methods, thermal analysis, physical property measurements, surface physics, membrane technology, microcomputer design, chip-based processes, and more. Readership includes everyone who uses instrumental techniques to conduct their research and development. They are chemists (organic, inorganic, physical, analytical, nuclear, quality control) biochemists, biotechnologists, engineers, and physicists in all of the instrumental disciplines mentioned above, in both the laboratory and chemical production environments. The journal is an important resource of instrument design and applications data.