Structural inspection is essential to improve the safety and sustainability of infrastructure systems, such as bridges. Therefore, several technologies have been developed to detect defects automatically and accurately. For example, instead of using naked eye for bridge surface defect detection, which is subjective and risky, Light Detection and Ranging can collect high-quality 3D point clouds.This paperpresents the Surface Normal Enhanced PointNet++ (SNEPointNet++),which is a modified version of thewell-knownPointNet++methodapplied to thetask of concrete surface defectdetection. To this end, a point clouddatasetfrom three bridges in Montreal was collected, annotated,and classified into the three classesofcracks, spalls, and no-defects. Based on the comparison between the results(IoU)from the proposed method and similar researchdoneon the same dataset, there areat least 54% and 13% performance improvementsin detecting cracks and spalls,respectively.
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