Dynamic ultrasonic waves can be utilized to assess concrete for defects. This study introduces an optimized approach using computer vision algorithms on ultrasound images to assist in localizing and quantifying substantial defects and components. To detect concrete details, the ultrasonic signals are assembled to create 3D images for the concrete material and then converted into 2D so-called B-scans. A computer vision algorithm was trained on a dataset of labeled images that show different levels of delamination as 2D ultrasound images. The algorithm can then analyze new ultrasound images and classify them according to the degree of delamination present. The computer vision techniques that can be used for this purpose include convolutional neural networks (CNN) by applying YOLO V5, commonly used for image classification tasks. In addition to detecting delamination, computer vision techniques can identify other abnormalities in concrete structures, such as cracks, voids, and inclusions. These techniques can help engineers and maintenance professionals to identify and address problems in concrete structures before they lead to more severe issues. The paper will present these methodologies and applications with a case study.