Oct 21, 2024 · This study combines a wall-climbing robot equipped with imaging capabilities to automate the detection and classification of cracks in concrete surfaces. We used machine learning to classify the crack orientation and OTSU to segment the crack shapes.

Dec 1, 2024 · This paper presents a practical and effective solution for the quantitative assessment of concrete bridge cracks through a systematic study that includes improvements to the crack segmentation network, maximization of the utilization of annotation resources, and image collection using unmanned robotic platforms.

Oct 13, 2023 · To overcome these challenges, our research focuses on developing an automated system that utilizes a wall-climbing robot for crack classification. Our main objective is to introduce a crack classification technique using MobileNetV2, enabling real-time classification without human intervention.

Sep 21, 2022 · The wall-climbing robot is able to detect concrete surface flaws (i.e., cracks and spalls) and produce a defect-highlighted 3D model with extracted location clues and metric measurements.

Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from real concrete surface images.

Wall climbing robot for crack detection in concrete buildings. A wall climbing detection robot has been developed that can stably adsorb and walk on concrete building walls, navigate to crack locations, and perform real-time recognition and data recording.

Sep 21, 2022 · The wall‐climbing robot is able to detect concrete surface flaws (i.e., cracks and spalls) and produce a defect‐highlighted 3D model with extracted location clues and metric measurements.

Sep 21, 2024 · This paper presents a mobile robot system equipped with a 2-DoF manipulator and stereo vision sensors for automated concrete crack monitoring. The stereo vision sensors serve dual purposes: crack analysis as well as autonomous navigation and mapping.

Jan 30, 2024 · Drexel researchers have created a multi-scale system that uses computer vision and machine learning programs to identify cracks in concrete and direct robotic scanning, modeling and monitoring. Our built environment is aging and failing faster than we can maintain it.

Therefore, this study proposes inspection system for measuring cracks in concrete structure and providing objective crack data to be used in safety assessment. The system consists of the mobile robot system and crack detecting system.