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![]() ![]() Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. ![]() Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Therefore, the automated crack detection algorithm is a key tool to improve the results. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement.
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