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An Integrated Deep Learning Framework for Road Distress Detection, Segmentation, and Quantitative Evaluation

Meng Xu, Wei Gao
Vol. 6, No. 2, pp. 20-30, 2026 DOI: 10.54097/hm8h2y49 February 28, 2026
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Abstract.Road distress inspection plays a critical role in pavement-condition assessment and maintenance planning. However, existing studies often address detection, segmentation, or measurement separately, which limits their practical applicability. This paper proposes an integrated deep learning–based framework for road distress detection, pixel-level segmentation, and quantitative evaluation. First, an improved YOLOv7 detector is developed by introducing SE attention, CARAFE-based content-aware upsampling, and a Dynamic Head to enhance multi-scale feature representation and robustness under complex road backgrounds. Second, a multi-scale encoder–decoder network termed MIResU-Net is designed to accurately extract crack and pothole regions with improved structural continuity and boundary precision. Finally, a calibration-based measurement strategy is employed to convert segmentation results into physically meaningful geometric parameters, such as crack length and pothole area. A real-world road-distress dataset collected by a vehicle-mounted system is constructed for comprehensive evaluation. Experimental results demonstrate that the proposed framework achieves superior detection and segmentation performance compared with mainstream methods and provides metrically reliable quantitative indicators for pavement-condition assessment. The proposed approach offers an effective and practical solution for intelligent road inspection.
Keywords. Road distress detection Deep learning YOLOv7 Image segmentation Quantitative evaluation
APA Citation Meng Xu, Wei Gao (2026). An Integrated Deep Learning Framework for Road Distress Detection, Segmentation, and Quantitative Evaluation. Journal of Computer Science and Artificial Intelligence, 6(2), 20-30. https://doi.org/10.54097/hm8h2y49

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