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YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images
Published 2025-06-01Subjects: “…asphalt pavement distresses…”
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Random forest-based frame work for multi-distress prediction in CRCP: a feature importance approach
Published 2025-08-01Subjects: Get full text
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Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads
Published 2025-05-01Subjects: Get full text
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Stereo Vision Method Application to Road Inspection
Published 2017-03-01Subjects: Get full text
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Vision-Based Automated Pavement Distress Inspection: A Review
Published 2025-01-01Subjects: “…Pavement distress inspection…”
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A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
Published 2025-05-01Subjects: “…pavement distress…”
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LSTM and TCN application for airport surface distress detection
Published 2025-09-01Subjects: Get full text
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The Road of Experimental Pavement Structures: Experience of Five Years Operation
Published 2012-09-01Subjects: Get full text
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Analysis and Classification of Distress on Flexible Pavements Using Convolutional Neural Networks: A Case Study in Benin Republic
Published 2025-04-01Subjects: Get full text
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Pavement Deterioration Modeling of the International Roughness Index Based on Fuzzy Logic Approach
Published 2025-07-01Subjects: Get full text
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Dual Model for International Roughness Index Classification and Prediction
Published 2025-01-01Subjects: Get full text
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Leveraging Maintenance Management Techniques to Evaluate Pavement Condition Index: Central Iraq Highway System
Published 2025-03-01Subjects: Get full text
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Deep Metric Learning-Based Classification for Pavement Distress Images
Published 2025-06-01Subjects: “…pavement distress detection…”
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Statistical analysis of an in-vehicle image-based data collection method for assessing airport pavement condition
Published 2025-07-01Subjects: Get full text
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Comparison of Pavement Performance Models for Urban Road Management System
Published 2020-08-01Subjects: Get full text
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