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IJEETC 2025 Vol.14(2): 108-114
doi: 10.18178/ijeetc.14.2.108-114

Enhancing of Features for Road Crack Image Using EEcGANs

Amal Mohammed Jaber and Farah Abbas Obaid Sari*
Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq
Email: amalm.alkreiti@student.uokufa.edu.iq (A.M.J.), faraha.altaee@uokufa.edu.iq (F.A.O.S.)
*Corresponding author

Manuscript received November 30, 2024; revised January 20, 2025; accepted January 24, 2025

Abstract—Continuously monitoring roads to examine their defects is a difficult task for the human element due to the increasing length of roads. Therefore, several types of digital imaging tools are used, for example (drones, surveillance cameras fixed on poles and cameras fixed on a moving car). These imaging tools automatically take pictures under various weather conditions and the blurriness generated by cameras mounted on moving cars. This paper proposes a method to enhance road surface images captured under different conditions, where the image will be improved in the frequency domain and spatial domain and then added to the enhancement with the Enlighten of Conditional Generative Adversarial Networks (EEcGANs) to obtain a generated and enhanced image. The metrics Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Difference in Variance (DIV), Correlation Coefficient (CC) and Universal Quality Index (UIQ) are used to evaluate the performance of the proposed method, achieving the values 29.4019, 0.8996, -0.12151, 972758 and 0.970491, respectively.

 
Index Terms—Conditional Generative Adversarial Networks (cGANs), evaluation metrics for the generated image, image enhancements methods, N-RDD2024 dataset

Cite: Amal Mohammed Jaber and Farah Abbas Obaid Sari, "Enhancing of Features for Road Crack Image Using EEcGANs," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 14, No. 2, pp. 108-114, 2025. doi: 10.18178/ijeetc.14.2.108-114

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.