Data Augmentation on Defect Detection of Sanitary Ceramics

Niu, Jiashen; Chen, Yifan; Yu, Xinghu; Zhan, Li; Gao, Huijun

doi:10.1109/iecon43393.2020.9254518

Cited by 12 publications

(1 citation statement)

References 21 publications

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“…In addition, the manual operation is less efficient, inconvenient to delete operations on the negative, and the negative classification and storage are time-consuming: all these problems greatly affect the efficiency of the radiographic inspection. Owing to the many shortcomings in the manual negative assessment process, it is increasingly unable to meet the needs of industrial automation and intelligent development [8]. erefore, the detection and evaluation of weld defects began to gradually develop from manual methods to computerized automatic identification and evaluation.…”

Section: Introductionmentioning

confidence: 99%

Image Defect Recognition Method Based on Deep Learning Network

2022

Mobile Information Systems

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Deep learning techniques are used to identify weld image defects in the process of image defect recognition. In this paper, a transfer learning method based on convolutional neural networks is proposed for the recognition problem of deep neural network models on weld flaw detection image data sets. Designing interdomain heterogeneous transfer learning with the pretrained model on the large data set, the interdomain heterogeneous transfer learning is used to transfer the pretrained model in the source data domain to the weld inspection image data set according to the difference of the content in the source and target data domains, and the effectiveness of the transfer learning in weld inspection image defect recognition is verified by fine-tuning the whole network by training the parameters of different layers using the frozen layer method. The effect of freezing different layers on the recognition performance of the model is also investigated.

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Section: Introductionmentioning

confidence: 99%

Image Defect Recognition Method Based on Deep Learning Network

2022

Mobile Information Systems

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Study on Analysis of Defect Identification Methods in Manufacturing Industry

Pal,

Mudholkar

2024

Communications in Computer and Information Science

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Scalable flood inundation mapping using deep convolutional networks and traffic signage

Alizadeh

Behzadan

2023

Comput.Urban Sci.

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Floods are one of the most prevalent and costliest natural hazards globally. The safe transit of people and goods during a flood event requires fast and reliable access to flood depth information with spatial granularity comparable to the road network. In this research, we propose to use crowdsourced photos of submerged traffic signs for street-level flood depth estimation and mapping. To this end, a deep convolutional neural network (CNN) is utilized to detect traffic signs in user-contributed photos, followed by comparing the lengths of the visible part of detected sign poles before and after the flood event. A tilt correction approach is also designed and implemented to rectify potential inaccuracy in pole length estimation caused by tilted stop signs in floodwaters. The mean absolute error (MAE) achieved for pole length estimation in pre- and post-flood photos is 1.723 and 2.846 in., respectively, leading to an MAE of 4.710 in. for flood depth estimation. The presented approach provides people and first responders with a reliable and geographically scalable solution for estimating and communicating real-time flood depth data at their locations.

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Data Augmentation on Defect Detection of Sanitary Ceramics

Cited by 12 publications

References 21 publications

Image Defect Recognition Method Based on Deep Learning Network

Image Defect Recognition Method Based on Deep Learning Network

Study on Analysis of Defect Identification Methods in Manufacturing Industry

Scalable flood inundation mapping using deep convolutional networks and traffic signage

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