| Title |
Analyzing Deep Learning-based Techniques in Concrete Crack Detection Technology |
| Authors |
김기웅(Kim, Ki-Woong) ; 유무영(Yoo, Moo-Young) |
| DOI |
https://doi.org/10.5659/JAIK.2024.40.3.285 |
| Keywords |
Crack detection; artificial intelligence; YOLO; crack; Performance Indicators |
| Abstract |
When buildings deteriorate, they may develop defects like surface cracks and structural subsidence. If left unaddressed, these issues can
significantly weaken the structure, potentially leading to collapse accidents. Detecting cracks promptly is crucial to prevent such outcomes.
With the advancements in artificial intelligence, researchers are exploring deep learning techniques to identify microscopic cracks, replacing
traditional manual methods. As AI technology progresses, diverse AI models have emerged, enhancing the reliability of crack detection data
for field inspections. This study focuses on leveraging the Yolo model, known for its superior performance and faster data acquisition
compared to other AI models. By incorporating object detection methods used by CNN, the study aims to enhance the detection performance
of the model by considering various variables across different AI models and detection techniques. |