Deep Learning Model for Determining Defects of Vision Inspection Machine Using Only a Few Samples

Jian, Bo-Lin; Hung, Jui‐Pin; Wang, Cheng‐Chi; Liu, Chun-Chang

doi:10.18494/sam.2020.3101

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Adibhatla et al [50] used a ResNet, Selmaier et al [46] used an Xception architecture, and Jian et al [69] used a DenseNet to classify whether damage is visible or not. Crack detection is addressed seven times with binary classification.…”

Section: Visual Inspection Via Binary Classificationmentioning

confidence: 99%

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Hütten,

Alves Gomes,

Hölken

et al. 2024

ASI

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Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.

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Section: Visual Inspection Via Binary Classificationmentioning

confidence: 99%

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Hütten,

Alves Gomes,

Hölken

et al. 2024

ASI

View full text Add to dashboard Cite

show abstract

“…(DL)-enhanced decision-making [1]. The surface of a defective product may present anomalies referrable to the composition of employed materials [2], the inclusion of foreign objects debris [3], and disturbance affecting the production phases, such as machinery and finishing processes [4] and transportation on conveyor belt [5]. Recently, massive investments have been devoted to the development of technologies that are able to automatically identify and localize defects by computing and further processing a suitable representation of raw data.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

et al. 2023

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Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.

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Feature-Based Transfer Learning for IoT-Enabled Defect Detection for Quality Control in Industrial Manufacturing Processes: A DenseNet Evaluation

P. P. Abdul Majeed,

Ateeq,

et al. 2024

Lecture Notes in Networks and Systems

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Deep Learning Model for Determining Defects of Vision Inspection Machine Using Only a Few Samples

Cited by 5 publications

References 19 publications

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Feature-Based Transfer Learning for IoT-Enabled Defect Detection for Quality Control in Industrial Manufacturing Processes: A DenseNet Evaluation

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