Adaptive vision inspection for multi-type electronic products based on prior knowledge

Delong, Zhao; Xue, Dun; Wang, Xiaoyao; Du, Fengming

doi:10.1016/j.jii.2021.100283

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…In addition, DL-based detection pipelines for specific parts can be found in [20,21]. A general scheme was proposed by [22] for the appearance quality inspection of various types of electronic products with small size. However, these studies have not emphasized the role and commonality of knowledge, which limits the reusability of their frameworks in other scenarios or tasks.…”

Section: Generic Vision Inspection Frameworkmentioning

confidence: 99%

“…The comparison between the method constructed according to this framework and advanced inspection schemes MTCI1 [80] and MTCI2 [22] for multi-type electrical connector is summarized in Table 11. IoU_1 * is employed to compare the performance of each group's initial matching work (recognition and registration in MTCI, Connection in this study).…”

Section: Validation On Different Scenario and Taskmentioning

confidence: 99%

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A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Zhao,

Kong,

et al. 2024

Sensors

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The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a challenge to the applicability of traditional machine vision technology. Extensive research demonstrates the effectiveness of AI-based learning and image processing on specific objects or tasks, but few publications focus on the composite task of the integrated product, the traceability and improvability of methods, as well as the extraction and communication of knowledge between different scenarios or tasks. To address this problem, this paper proposes a common, knowledge-driven, generic vision inspection framework, targeted for standardizing product inspection into a process of information decoupling and adaptive metrics. Task-related object perception is planned into a multi-granularity and multi-pattern progressive alignment based on industry knowledge and structured tasks. Inspection is abstracted as a reconfigurable process of multi-sub-pattern space combination mapping and difference metric under appropriate high-level strategies and experiences. Finally, strategies for knowledge improvement and accumulation based on historical data are presented. The experiment demonstrates the process of generating a detection pipeline for complex products and continuously improving it through failure tracing and knowledge improvement. Compared to the (, 69.802 mm) and 0.883 obtained by state-of-the-art deep learning methods, the generated pipeline achieves a pose estimation ranging from (, 153.584 mm) to (, 52.308 mm) and a detection rate ranging from 0.462 to 0.927. Through verification of other imaging methods and industrial tasks, we prove that the key to adaptability lies in the mining of inherent commonalities of knowledge, multi-dimensional accumulation, and reapplication.

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Section: Generic Vision Inspection Frameworkmentioning

confidence: 99%

Section: Validation On Different Scenario and Taskmentioning

confidence: 99%

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Zhao,

Kong,

et al. 2024

Sensors

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“…Table 8 presents machine learning algorithms of each informed machine learning category. Articles were mainly published after 2017, and a great part of them concern neural networks (Hassanzadeh et al, 2020;Gaur et al, 2019;Ali et al, 2019;Jang et al, 2018;Zhang et al, 2019;Ali et al, 2021;Amador-Domínguez et al, 2021;Benarab et al, 2019;Chen et al, 2021;Wang et al, 2021bWang et al, , 2010Sabra et al, 2020;Pancerz and Lewicki, 2014;Yilmaz, 2017;Kumar et al, 2020;Rinaldi et al, 2021;Gomathi and Karlekar, 2019;Serafini et al, 2017;Kuang et al, 2021;Chung et al, 2020;Fu et al, 2015;Huang et al, 2019;Abdollahi et al, 2021;Ahani et al, 2021;Akila et al, 2021;Deepak et al, 2022;Messaoudi et al, 2021;Nayak et al, 2021;Zhao et al, 2021), especially Recurrent Neural Networks (Makni and Hendler, 2019;Ren et al, 2020;Moussallem et al, 2019;Zhang et al, 2019;Jang et al, 2018;Ali et al, 2021;Liu et al, 2021;Huang et al, 2019;Alexandridis et al, 2021;Niu...…”

Section: Informed Machine Learningmentioning

confidence: 99%

“…Feature selection aims at reducing the number of variables to keep only the most relevant without changing the initial variables (Gomathi and Karlekar, 2019;Mendez et al, 2019). On the contrary, Feature extraction change the initial variables using the prior knowledge of the ontology for get relevant features (Kumar et al, 2020;Radovanovic et al, 2019;Evert et al, 2019;Agarwal et al, 2015;Radinsky et al, 2012;Greenbaum et al, 2019;Liu et al, 2021;Rinaldi et al, 2021;Castillo et al, 2008;Yilmaz, 2017;Hsieh et al, 2013;Rajput and Haider, 2011;Manuja and Garg, 2015;Ahani et al, 2021;Akila et al, 2021;Deepak et al, 2022;Messaoudi et al, 2021;Nayak et al, 2021;Pérez-Pérez et al, 2021;Zhao et al, 2021;. In semantic embedding, always in training data step, raw data are both refined by semantic knowledge and transformed into vectors to be exploited by neural networks (Chen et al, 2021;Ren et al, 2020;Qiu et al, 2019;Ali et al, 2019;Zhang et al, 2019;Makni and Hendler, 2019;Benarab et al, 2019;Moussallem et al, 2019;Gaur et al, 2019;Jang et al, 2018;Hassanzadeh et al, 2020;Ali et al, 2021;Amador-Domínguez et al, 2021;Alexandridis et al, 2021;Niu et al, 2022), SVM…”

Section: Informed Machine Learningmentioning

confidence: 99%

Mixed artificial reasoning, closer to human?

Ghidalia

Narsis

Bertaux

et al. 2022

Preprint

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AI is now used in many areas of our daily life, however we still have difficulties to make machines interact with our world as we perceive it. Humans mainly use two types of reasoning to grasp reality: inductive and deductive reasoning. In AI, inductive reasoning is possible thanks to the use of machine learning models, while deductive reasoning can be provided by the use of ontologies. Based on these statements, we have conducted a review of articles combining the use of machine learning and ontologies in order to identify the most recent techniques that combine induction and deduction within an AI. This systematic literature review allowed us, after reading 128 studies, to identify three main groups of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining and learning and reasoning system. The combination of these two types of reasoning allows machines to be better informed about the specificity of our world.

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A multi-task effectiveness metric and an adaptive co-training method for enhancing learning performance with few samples

Wang,

Du,

Zhao

et al. 2024

J Intell Manuf

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Adaptive vision inspection for multi-type electronic products based on prior knowledge

Cited by 5 publications

References 34 publications

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Mixed artificial reasoning, closer to human?

A multi-task effectiveness metric and an adaptive co-training method for enhancing learning performance with few samples

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