Flaw Detection from Ultrasonic Images using YOLO and SSD

Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans

“…However, a B-scan usually consists of hundreds of A-scans which further aggravates the problem of lack of data. Developed algorithms for defect detection can be divided into three groups related to data representation being used; A-scans [14,15,16,17,18,19,20,7,21,22,8,23], B-scans [24,25,6,5] and C-scans [26,27]. The A-scan analysis is the most researched group of all which is also related to the data problem.…”

“…B-scans keep the geometrical coherence of the defect which leads to a better noise immunity [24]. However, the analysis of B-scans can only be seen in a few works [5,6]. In [5] two popular deep learning object detection models, YOLOv3 and SSD, have been used for defect detection.…”

“…However, the analysis of B-scans can only be seen in a few works [5,6]. In [5] two popular deep learning object detection models, YOLOv3 and SSD, have been used for defect detection. In [6] a deep learning detector has been tested on augmented images, but with only three defects in the specimen block.…”

“…Ultrasonic data can be represented in several different formats suitable for analysis including A, B, or C-scans [4]. An A-scan is an ultrasonic signal's amplitude put as a function of time, B-scan displays a cross-sectional view of the inspected material, and a C-scan provides a top view of its projected features [5]. During analysis, inspectors use all of them in order to make a decision and evaluate the data.…”

“…The automated analysis could make the process much faster and reliable. There have been some attempts in developing an automated UT analysis [7,8,9,6,5], but very few of them involve using deep learning and modern deep convolutional neural networks (CNNs) on B-scans. The prerequisite for using deep learning is a large, annotated dataset.…”