Abstract :

Road Infrastructure Maintenance Plays A Critical Role In Ensuring Transportation Safety And Reducing Long-term Maintenance Costs. Early Identification Of Pavement Cracks Is Essential To Prevent Structural Deterioration And Improve Road Quality. This Paper Presents An Intelligent Road Crack Detection System Based On The DeepLabV3+ Semantic Segmentation Architecture For Accurate And Automated Pavement Damage Analysis. The Proposed System Utilizes Deep Learning And Computer Vision Techniques To Identify And Segment Crack Regions From Road Surface Images At The Pixel Level. The Framework Employs DeepLabV3+, Which Integrates Atrous Spatial Pyramid Pooling (ASPP) And Dilated Convolutions To Capture Multi-scale Contextual Information While Preserving Fine Spatial Details. The Model Is Trained On Annotated Road Crack Datasets And Optimized To Detect Thin, Irregular, And Complex Crack Patterns Under Varying Lighting And Surface Conditions. A ResNet-based Classification Module Is Incorporated To Determine The Presence Of Cracks Before Segmentation, Improving Computational Efficiency. Furthermore, A PyQt5-based Graphical User Interface Is Developed To Provide An Intuitive Platform For Image Upload, Crack Detection, Segmentation Visualization, And Result Interpretation. The Implementation Is Carried Out Using TensorFlow, Keras, OpenCV, NumPy, And PyQt5, Enabling An End-toend Automated Inspection Workflow. Experimental Results Demonstrate That The DeepLabV3+ Model Significantly Outperforms Conventional Approaches, Achieving High Segmentation Accuracy, Improved Intersection-over-Union (IoU), And Enhanced Robustness Against Noise And Environmental Variations. The Proposed System Offers A Scalable And Cost-effective Solution For Intelligent Road Monitoring And Can Be Integrated With Modern Infrastructure Inspection Platforms Such As Drones, Mobile Imaging Systems, And Smart Transportation Networks.

Published:

11-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

524-537

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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