A Study of Comparison Between YOLOv5 and YOLOv7 for Detection of Cracks in Concrete Structures

A Study of Comparison Between YOLOv5 and YOLOv7 for Detection of Cracks in Concrete Structures

연구 분야: Strategies

학회: International Conference on Computer Vision, High-Performance Computing, Smart Devices, and Networks

Mechanical failures of man-made structures pose risk to human life and property. Location and mitigation of cracks before it propagates through critical regions of a mechanical/civil structure prevents the occurrence of accidents and loss of life. Crack propagation, also known as sub-critical crack propagation or stress corrosion, frequently happens under low stress and is characterized by gradual propagation. By releasing the elastic strain energy brought on by an external load creates the formation of new surfaces. Cracks spread to reduce the energy of the system. Surface cracks can be found using various non-destructive testing methods: Visual Optical Testing, Eddy Current Testing, Liquid Penetrant Testing, and Magnetic Particle Testing. This study is currently limited to the use of visual testing using computer vision, feature extraction from captured data using multiple image processing algorithms to identify cracks using an object detector model build using data points collected from a user data set. In a systematic manner, we tried to develop object detector models separately using YOLOv5 and YOLOv7 and performed a study on different standard evaluation metrics obtained from the two frameworks. YOLOv5 and YOLOv7 are the two recent additions to the YOLO family. YOLO-based object detector uses deep learning to train models for object detection using pre-trained weights from COCO data set. Results indicate further application of the detector model to be assertive on any physical structures pertaining to risk of surface cracks.