Special Features on Intelligent Imaging and Analysis

Hwang, Dosik; Kim, DaeEun

doi:10.3390/app9224804

Cited by 2 publications

(3 citation statements)

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“…The CrackForest [2,3] dataset consists of 118 labeled color images taken with an iPhone 5 camera containing noise: Oil stains, road marking, shoe contours, and shadows. Images are 480 × 320 8 bit RGB.…”

Section: Crackforestmentioning

confidence: 99%

“…Difficulties of defect identification can be seen in many manufacturing areas; solutions and the newest method applications were presented in [3] with intelligent imaging and analysis techniques applied in various research fields. The authors in [1] reviewed DL network application publications on pavement crack detection since its first appearance in 2016.…”

Section: Introductionmentioning

confidence: 99%

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Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

Augustauskas

Lipnickas

2020

Sensors

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Convolutional neural networks perform impressively in complicated computer-vision image-segmentation tasks. Vision-based systems surpass humans in speed and accuracy in quality inspection tasks. Moreover, the maintenance of big infrastructures, such as roads, bridges, or buildings, is tedious and time-demanding work. In this research, we addressed pavement-quality evaluation by pixelwise defect segmentation using a U-Net deep autoencoder. Additionally, to the original neural network architecture, we utilized residual connections, atrous spatial pyramid pooling with parallel and “Waterfall” connections, and attention gates to perform better defect extraction. The proposed neural network configurations showed a segmentation performance improvement over U-Net with no significant computational overhead. Statistical and visual performance evaluation was taken into consideration for the model comparison. Experiments were conducted on CrackForest, Crack500, GAPs384, and mixed datasets.

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Section: Crackforestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

Augustauskas

Lipnickas

2020

Sensors

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“…The existing algorithms for visual detection of defects on the road can be conditionally divided into two branches: traditional methods [3] for detecting defects and methods of artificial intelligence [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Semantic Convolution Neural Networks on the Example of Crack Segmentation in Asphalt Images

Akimov¹,

Мустафина²,

Plotnikova³

2021

IJC

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The article is devoted to a comparative analysis of the effectiveness of convolutional neural networks for semantic segmentation of road surface damage marking. Currently, photo and video surveillance methods are used to control the condition of the road surface. Assessing and analyzing new manual data can take too long. Thus, a completely different procedure is required to inspect and assess the state of controlled objects using technical vision. The authors compared 3 neural networks (Unet, Linknet, PSPNet) used in semantic segmentation using the example of the Crack500 dataset. The proposed architectures have been implemented in the Keras and TensorFlow frameworks. The compared models of neural convolutional networks effectively solve the assigned tasks even with a limited amount of training data. High accuracy is observed. The considered models can be used in various segmentation tasks. The results obtained can be used in the process of modeling, monitoring, and predicting the wear of the road surface.

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Special Features on Intelligent Imaging and Analysis

Abstract: Intelligent imaging and analysis have been studied in various research fields, including medical imaging, biomedical applications, computer vision, visual inspection and robot systems [...]

Cited by 2 publications

References 30 publications

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

Comparison of Semantic Convolution Neural Networks on the Example of Crack Segmentation in Asphalt Images

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