切桃子:食品加工中实用模式挖掘的图像分割

Diletta Chiaro, E. Prezioso, Stefano Izzo, F. Giampaolo, S. Cuomo, F. Piccialli
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引用次数: 0

摘要

信息和通信技术领域取得的进展,特别是在计算机科学领域取得的进展,以及新型计算系统(云/边缘计算)不断增长的能力,极大地促进了网络物理系统的发展,在网络物理系统中,协作计算实体与周围物理环境及其持续运行紧密相连。所有这些都增加了自动完成迄今为止被认为是人类独有的任务的可能性:因此,许多公司逐渐采用人工智能(AI)和机器学习(ML)技术来实现人类活动自动化的趋势。本文属于深度学习(DL)的实用模式挖掘应用于工业4.0的背景下。从一家从事食品加工行业的跨国公司提供的图像开始,我们提供了一个用于实时模式识别的深度学习框架,该框架应用于桃子打罐自动化。为此,我们通过将七个预训练的编码器嵌入到多个分割架构中来执行图像分割的迁移学习(TL),并根据数据的度量和推理速度评估和比较分割性能。此外,我们提出了一种注意机制,通过注意引导的特征聚合来改善FPN中的多尺度特征学习。
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Cut the peaches: image segmentation for utility pattern mining in food processing
The progress achieved in the field of information and communication technologies, particularly in computer science, and the growing capacity of new types of computational systems (cloud/edge computing) significantly contributed to the cyber-physical systems, networks where cooperating computational entities are intensively linked to the surrounding physical en-vironment and its on-going operations. All that has increased the possibility of undertaking tasks hitherto considered to be an exclusively human concern automatically: hence the gradual yet progressive tendency of many companies to adopt artificial intelligence (AI) and machine learning (ML) technologies to automate human activities. This papers falls within the context of deep learning (DL) for utility pattern mining applied to Industry 4.0. Starting from images supplied by a multinational company operating in the food processing industry, we provide a DL framework for real-time pattern recognition applied in the automation of peach pitters. To this aim, we perform transfer learning (TL) for image segmentation by embedding seven pre-trained encoders into multiple segmentation architectures and evaluate and compare segmentation performance in terms of met-rics and inference speed on our data. Furthermore, we propose an attention mechanism to improve multiscale feature learning in the FPN through attention-guided feature aggregation.
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