A Logical Examination of Roadway Crack Detection and Classification Methodology using Improved Neural Network Strategy

Background: Roadway crack detection is a significant aspect of modern infrastructure management, essential for maintaining road safety and extending the lifespan of roadways through timely maintenance. Cracks on road surfaces can occur due to various factors such as traffic load, environmental conditions, aging, and subgrade settlement. Early detection and proper classification of these cracks are crucial for performing timely repairs, preventing further deterioration, and avoiding costly reconstructions. Traditional methods are time-consuming, labor-intensive, and prone to human error. Methods: This paper presents an improved methodology using Advanced Hybrid Neural Network (AHN-Net) strategies for automated crack detection and classification. High-resolution images of road surfaces are processed using deep learning models to identify and categorize various types of cracks. Results: Our proposed method achieves an accuracy of 97%, significantly enhancing detection precision and efficiency. This approach reduces the need for manual inspections, improving overall road maintenance strategies.