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Super-Resolution Reconstruction Method of Pavement Crack Images Based on an Improved Generative Adversarial Network.

Bo Yuan¹, Zhaoyun Sun¹, Lili Pei¹

¹School of Information Engineering, Chang'an University, Xi'an 710064, China.

Sensors (Basel, Switzerland)

|December 11, 2022

Summary

This study introduces an improved generative adversarial network for super-resolution reconstruction, enhancing pavement crack detection accuracy. The new method significantly boosts image quality and detection confidence in intelligent pavement systems.

Keywords:

GAN deep Learning image reconstruction pavement crack image super-resolution

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Intelligent pavement detection faces challenges with image quality variations due to equipment and lighting.
Existing super-resolution methods struggle with the disparities in pavement image data.

Purpose of the Study:

To develop a super-resolution reconstruction approach for improving pavement crack detection.
To enhance image quality for more accurate intelligent pavement analysis.

Main Methods:

An improved generative adversarial network (GAN) was utilized, featuring a nonlinear generator network.
Residual Dense Blocks (RDB) were incorporated for Batch Normalization (BN), and an Attention Module combined RDB, Gated Recurrent Unit (GRU), and Conv Layer.
A loss function based on the L1 norm replaced the original loss function.

Main Results:

Reconstructed images achieved a Peak Signal-to-Noise Ratio (PSNR) of 29.21 dB and Structural Similarity (SSIM) of 0.854 on a pavement crack dataset.
The approach demonstrated improvements over standard datasets like Set5, Set14, and BSD100.
Segmentation F1 score improved to 0.737 and detection confidence to 0.9102 when compared to state-of-the-art methods.

Conclusions:

The proposed super-resolution reconstruction method effectively addresses image quality issues in pavement detection.
The enhanced image quality leads to significant improvements in pavement crack segmentation and detection accuracy.
This technique holds substantial engineering value for intelligent pavement management systems.