Generation of CNN Architectures Using the Harmonic Search Algorithm and its Application to Classification of Damaged Sewer

Jeong, Yong-Woon; Sim, Kwee-Bo; Park, Seungmin; Oh, Jeill; Choi, Woo June

doi:10.1109/access.2022.3160719

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Recently, the field has also started investigating other parts of the sewer inspection process [30,32,17,[37][38][39][40][41], such as Haurum et al [37] proposing a multi-task classification approach for simultaneously classifying defects, water level, pipe material, and pipe shape, and Wang et al [30] proposed a framework to accurately determine the severity of defects related to the operation and maintenance of the pipes. The field has also adopted recent trends from the general computer vision field such as selfsupervised learning [39], synthetic data generation [25,24,[42][43][44], neural architecture search [45], and usage of the Transformer architecture [17,46], indicating that the automated sewer inspection field is catching up to the general computer vision domain.…”

Section: Automated Sewer Inspectionsmentioning

confidence: 99%

Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification

Haurum

Madadi

Escalera

et al. 2022

Automation in Construction

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Section: Automated Sewer Inspectionsmentioning

confidence: 99%

Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification

Haurum

Madadi

Escalera

et al. 2022

Automation in Construction

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Automated urban landscape design: an AI-driven model for emotion-based layout generation and appraisal

Tang,

Chung

2024

PeerJ Computer Science

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The evolution of a city is significantly shaped by the design of its urban landscape. The advancement of artificial intelligence has substantially increased convenience for individuals. This research proposes an urban landscape layout model powered by artificial intelligence that automatically generates urban landscape design based on deep learning (URDDL) with two dimensions: emotional tendency and urban landscape appraisal. The input image represents land use and surrounding road conditions, while the output image depicts the selection of the main entrance and the internal spatial function layout. The Pix2Pix model is trained to learn the internal function layout based on varying land use and road conditions. Additionally, a domain-specific dictionary is constructed using an existing semantic resource vocabulary, where positive and negative sentiment words are compared with their corresponding sentiment values, focusing on categories such as Stimulate, Sense, and Action. Experimental results indicate that the absolute average error of the URDDL model is 91.31%, with a maximum error of 96.87%. The degree of fit is highly appropriate for evaluating the emotional prediction of urban landscapes. The findings demonstrate that the URDDL model outperforms traditional design methods regarding generated results, suggesting its potential for future applications in automated landscape design.

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Generation of CNN Architectures Using the Harmonic Search Algorithm and its Application to Classification of Damaged Sewer

Abstract: KEITI) through project for developing innovative drinking water and wastewater technologies, funded by Korea Ministry of Environment (MOE) (2020002700015).

Cited by 2 publications

References 34 publications

Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification

Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification

Automated urban landscape design: an AI-driven model for emotion-based layout generation and appraisal

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