Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing

Jung, Hwee Kwon; Park, Gyuhae

doi:10.1177/1475921718811157

Cited by 15 publications

(8 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Below are some related works for concrete analysis that use image-processing techniques. Reference [21] described an online image-processing-based technique for rapidly and non-invasively detecting cracks in pressed-panel goods; however, this method used a standard camera, and the dataset was relatively small. An algorithm developed in [22] presented a feature detection approach that uses the Sobel operator to filter and denoise concrete images before implementing the Otsu method for thresholding segmentation for crack edge identification.…”

Section: Related Implementationmentioning

confidence: 99%

Section: Related Implementationmentioning

confidence: 99%

See 1 more Smart Citation

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Kim

Choi

et al. 2021

Sensors

View full text Add to dashboard Cite

With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object’s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.

show abstract

Section: Related Implementationmentioning

confidence: 99%

Section: Related Implementationmentioning

confidence: 99%

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Kim

Choi

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…To overcome these issues, researchers have applied different techniques for eliminating the effect of varying conditions. 24–27 Fujita and Hamamoto 28 used a median filter to remove shadings from concrete surface images and a line filter with Hessian matrix to enhance cracks and then used probabilistic relaxation for crack detection. Iyer and Sinha 25 found that cracks in pipe images are darkest in the images, locally linear, and have tree-like geometry.…”

Section: Related Workmentioning

confidence: 99%

Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones

Mei

Gül

2020

Structural Health Monitoring

View full text Add to dashboard Cite

Cracks are important signs of degradation in existing infrastructure systems. Automatic crack detection and segmentation plays a key role in developing smart infrastructure systems. However, this field has been challenging over the last decades due to irregular shape of the cracks and complex illumination conditions. This article proposes a novel deep-learning architecture for crack segmentation at pixel-level. In this architecture, one convolutional layer is densely connected to multiple other layers in a feed-forward fashion. Max pooling layers are used to reduce the dimensions of the features, and transposed convolution layers are used for multi-level feature fusion. A depth-first search–based algorithm is applied as post-processing tool to remove isolated pixels and improve the accuracy. The method is tested on two previously published data sets. It can reach 92.02%, 91.13%, and 91.58% for the first data set, and 92.17%, 91.61%, and 91.89% for the second data set for precision, recall, and F1 score, respectively. The performance of the proposed method outperforms other state-of-the-art methods. At the end of the article, the influence of feature fusion methods and transfer learning are also discussed.

show abstract

“…Domestic and foreign scholars have achieved fruitful results on image processing of ecommerce products [8][9][10]. To extract the exact text information in ecommerce images, Wasim et al…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing

Wei¹,

Zhang²

2021

View full text Add to dashboard Cite

The new retail is an industry featured by online ecommerce. One of the key techniques of the industry is the product identification based on image processing. This technique has an important business application value, because it is capable of improving the retrieval efficiency of products and the level of information supervision. To acquire high-level semantics of images and enhance the retrieval effect of products, this paper explores the feature extraction and retrieval of ecommerce product images based on image processing. The improved Fourier descriptor was innovatively into a metric learning-based product image feature extraction network, and the attention mechanism was introduced to realize accurate retrieval of product images. Firstly, the authors detailed how to acquire the product contour and the axis with minimum moment of inertia, and then extracted the shape feature of products. Next, a feature extraction network was established based on the metric learning supervision, which is capable of obtaining distinctive feature, and thus realized the extraction of distinctive and classification features of products. Finally, the authors expounded on the product image retrieval method based on cluster attention neural network. The effectiveness of our method was confirmed through experiments. The research results provide a reference for feature extraction and retrieval in other fields of image processing.

show abstract

Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing

Cited by 15 publications

References 49 publications

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones

Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing

Contact Info

Product

Resources

About