Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification

Maintaining reliable and long-lasting road infrastructure requires accurate identification and management of pavement cracks, as these cracks can significantly weaken asphalt and concrete surfaces over time. Although Convolutional Neural Networks (CNNs) and meta-heuristic algorithms have proven effective in solving real-world problems, their use in low-contrast pavement crack images is worth investigating. This study proposes an automated crack detection framework that integrates three key components: (1) a new variant of a pre-trained CNN architecture, referred to as Modified EfficientNetV2 (MEfficientNetV2) for pavement crack classification; (2) a combination of opposition-based learning with White Shark Optimizer (WSO), known as Opposition WSO (OWSO), to improve the balance between exploration and exploitation; and (3) Principal Component Analysis (PCA) for efficient dimensionality reduction and feature selection. This method is validated on various publicly available asphalt crack datasets that contain low-contrast natural images. Preprocessing techniques are first applied to eliminate noise and enhance image quality. The OWSO algorithm is then integrated to optimize the classification performance of MEfficientNetV2, while PCA accelerates the learning process by retaining critical features in the thresholds of the varying components. Comparative evaluations with state-of-the-art methods demonstrate that the proposed model excels in terms of precision, robustness, and generalizability. The outcome emphasizes its ability to identify the most effective solution for crack detection in practical scenarios, where PCA-based feature selection improves computational efficiency without compromising performance. This study focuses on the potential of hybrid deep learning and bio-inspired optimization strategies to improve automated pavement maintenance systems.