An Improved Convolutional Neural Network Architecture for Image Classification

Ferreyra-Ramírez, Andrés; Avilés‐Cruz, Carlos; Rodriguez-Martinez, Eduardo; Villegas-Cortéz, Juan; Zúñiga-López, Arturo

doi:10.1007/978-3-030-21077-9_9

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…That AI is able to outperform humans especially in non-structured data case was shown especially for with DL for computer vision (i.e. how computers can gain high-level understanding from digital images or videos) in related scientific fields [29,30]. Quality management processes with visual inspections components are often organized to probe the whole production process through several human-based controls of product specific quality measures.…”

Section: Ai For Quality Assurance and Inspection Of Glass Productsmentioning

confidence: 99%

Connecting Artificial Intelligence and Structural Glass Engineering – Overview, Potentials and Case Studies

Kraus

Drass

2021

ce papers

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This paper introduces the Artificial Intelligence (AI) technology to structural glass engineering and glass industry audience. The first part of the paper is concerned with lying nomenclature and theory foundation of AI and its subclasses of Machine and Deep Learning (ML/DL), elaborating the specific needs and requirements for the application in a structural glass context. A subsequent section explores applications of AI for different subjects within the production and quality assessment of glass products as well as the design, verification and monitoring of facades and glass structures. This paper presents successfully conducted industry projects by the authors, which are: supervised ML for material parameter identification of polymeric interlayers used in laminated glass, the prediction of sound insulation properties of insulation glass units and glass laminates and the application of computer vision DL methods to image classification of the Pummel test. A visionary outlook highlights how to use AI for future generative design and verification of glass structures for rapid collaborative prototyping. The summary and conclusion section wraps up the main findings for the applicability and

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Section: Ai For Quality Assurance and Inspection Of Glass Productsmentioning

confidence: 99%

Connecting Artificial Intelligence and Structural Glass Engineering – Overview, Potentials and Case Studies

Kraus

Drass

2021

ce papers

View full text Add to dashboard Cite

show abstract

“…This approach was already proved successful in related scientific fields involving AI and especially DL for computer vision (i.e. how computers can gain high-level understanding from digital images or videos), where it clearly outperformed humans in several areas (Voulodimos et al 2018;Ferreyra-Ramirez et al 2019).…”

Section: Ai For Inspection and Control Of Glass Productsmentioning

confidence: 99%

“…In addition to the enormous time and hence economic savings, the objectivity and reproducibility of detection is an important aspect of improvement. The topic of image classification in the context of computer vision and DL is well known (Ferreyra-Ramirez et al 2019). As stated in the previous section of this paper, image classification is concerned with classifying images based on its visual content.…”

Section: Ai Prediction Of Cut-edge Of Glass Via Semantic Segmentationmentioning

confidence: 99%

Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

Kraus

Drass

2020

Glass Struct Eng

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’Big data’ and the use of ’Artificial Intelligence’ (AI) is currently advancing due to the increasing and even cheaper data collection and processing capabilities. Social and economical change is predicted by numerous company leaders, politicians and researchers. Machine and Deep Learning (ML/DL) are sub-types of AI, which are gaining high interest within the community of data scientists and engineers worldwide. Obviously, this global trend does not stop at structural glass engineering, so that, the first part of the present paper is concerned with introducing the basic theoretical frame of AI and its sub-classes of ML and DL while the specific needs and requirements for the application in a structural engineering context are highlighted. Then this paper explores potential applications of AI for different subjects within the design, verification and monitoring of façades and glass structures. Finally, the current status of research as well as successfully conducted industry projects by the authors are presented. The discussion of specific problems ranges from supervised ML in case of the material parameter identification of polymeric interlayers used in laminated glass or the prediction of cut-edge strength based on the process parameters of a glass cutting machine and prediction of fracture patterns of tempered glass to the application of computer vision DL methods to image classification of the Pummel test and the use of semantic segmentation for the detection of cracks at the cut edge of glass. In the summary and conclusion section, the main findings for the applicability and impact of AI for the presented structural glass research and industry problems are compiled. It can be seen that in many cases AI, data, software and computing resources are already available today to successfully implement AI projects in the glass industry, which is demonstrated by the many current examples mentioned. Future research directories however will need to concentrate on how to introduce further glass-specific theoretical and human expert knowledge in the AI training process on the one hand and on the other hand more pronunciation has to be laid on the thorough digitization of workflows associated with the structural glass problem at hand in order to foster the further use of AI within this domain in both research and industry.

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Faster-YOLO: An accurate and faster object detection method

Yin

2020

Digital Signal Processing

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An Improved Convolutional Neural Network Architecture for Image Classification

Cited by 5 publications

References 13 publications

Connecting Artificial Intelligence and Structural Glass Engineering – Overview, Potentials and Case Studies

Connecting Artificial Intelligence and Structural Glass Engineering – Overview, Potentials and Case Studies

Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

Faster-YOLO: An accurate and faster object detection method

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