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Corresponding Author ORCID Identifer

Arselan Ashrafhttps://orcid.org/0000-0001-7636-7130

Section

Regular Issue

Abstract

Pavement distress analysis plays a big role in keeping roads in good shape, especially in busy spots like Selangor and Kuala Lumpur, where heavy traffic and tropical weather make them wear out fast. This work introduces DeepSeg-CrackNet, a fresh hybrid deep learning model that uses Deep Gradient ResNet to spot cracks and a Residual block with a Modified Attention Mechanism to sort them into types, making it simpler to detect and label pavement damage. The model was trained on real data collected from Malaysian roads, with the CRACK500 dataset added in to cover more situations, and captured using a GoPro Hero 8 mounted on a vehicle, with GPS mapping keeping everything clear and easy to trace. DeepSeg-CrackNet performs really well—it hits a Mean IoU of 0.8388889 for segmentation and scores 85% accuracy in classifying cracks like alligator, longitudinal, and transverse, with precision ranging from 0.84 to 0.89, and recall between 0.80 and 0.96. It also measures cracks in meters or square meters, which helps in planning repairs smartly, like replacing big alligator cracks or sealing smaller longitudinal ones to save resources. Compared to models like CrackNet, DeepSeg-CrackNet stands out, especially for alligator cracks, with a precision of 0.84 and recall of 0.96, beating CrackNet’s 0.778 and 0.772. In the end, DeepSeg-CrackNet makes it easier to manage Malaysia’s roads in a data-driven way, improving safety and ensuring longer-lasting infrastructure through smarter, proactive repair approaches that enhance city travel.

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