Neshat Bolourian, M. Nasrollahi, Fardin Bahreini, A. Hammad
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引用次数: 3
Abstract
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.
期刊介绍：
The Journal of Computing in Civil Engineering serves as a resource to researchers, practitioners, and students on advances and innovative ideas in computing as applicable to the engineering profession. Many such ideas emerge from recent developments in computer science, information science, computer engineering, knowledge engineering, and other technical fields. Some examples are innovations in artificial intelligence, parallel processing, distributed computing, graphics and imaging, and information technology. The journal publishes research, implementation, and applications in cross-disciplinary areas including software, such as new programming languages, database-management systems, computer-aided design systems, and expert systems; hardware for robotics, bar coding, remote sensing, data mining, and knowledge acquisition; and strategic issues such as the management of computing resources, implementation strategies, and organizational impacts.
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