HDR Image Compression by Multi-Scale down Sampling of Intensity Levels

Swamy, A. S. Anand; Shylashree, N.

doi:10.1142/s0219467821500480

Cited by 1 publication

(1 citation statement)

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“…The discriminator undertakes seven down-sampling operations when presented with both the real bridge crack image and the generated crack image. These operations efficiently diminish both the size of the images and the dimensions of their respective features [36]. Each convolution layer employs convolution kernels measuring 4 × 4.…”

Section: The Architecture Of the Dcganmentioning

confidence: 99%

A Rapid Bridge Crack Detection Method Based on Deep Learning

Liu,

Gao,

Zhao

et al. 2023

Applied Sciences

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The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concrete crack images were generated by a deep convolutional generative adversarial network (DCGAN), and the artificial samples were balanced and feature-rich. Then, the dataset was established by mixing the artificial samples with the original samples. You Only Look Once v5 (YOLOv5) was trained on this dataset to implement rapid detection of concrete bridge cracks, and the detection accuracy was compared with the results using only the original samples. The experiments show that DCGAN can mine the potential distribution of image data and extract crack features through the deep transposed convolution layer and down sampling operation. Moreover, the light-weight YOLOv5 increases channel capacity and reduces the dimensions of the input image without losing pixel information. This method maintains the generalization performance of the neural network and provides an alternative solution with a low cost of data acquisition while accomplishing the rapid detection of bridge cracks with high precision.

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Section: The Architecture Of the Dcganmentioning

confidence: 99%