For many decades, ultrasonic imaging inspection has been adopted as a principal method to detect multiple defects, e.g., void and corrosion. However, the data interpretation relies on an inspector’s subjective judgment, thus making the results vulnerable to human error. Nowadays, advanced computer vision techniques reveal new perspectives on the high-level visual understanding of universal tasks. This research aims to develop an efficient automatic ultrasonic image analysis system for nondestructive testing (NDT) using the latest visual information processing technique. To this end, we first established an ultrasonic inspection image dataset containing 6849 ultrasonic scan images with full defect/no-defect annotations. Using the dataset, we performed a comprehensive experimental comparison of various computer vision techniques, including both conventional methods using hand-crafted visual features and the most recent convolutional neural networks (CNN) which generate multiple-layer stacking for representation learning. In the computer vision community, the two groups are referred to as shallow and deep learning, respectively. Experimental results make it clear that the deep learning-enabled system outperformed conventional (shallow) learning schemes by a large margin. We believe this benchmarking could be used as a reference for similar research dealing with automatic defect detection in ultrasonic imaging inspection.

Drone-based inspections provide an efficient and flexible approach to assessing aging infrastructures while prioritizing safety. Here, we present a pioneering framework that employs drone cameras for high-precision displacement measurement and achieves sub-millimeter accuracy, meeting the requirements for on-site inspections. Inspired by the principles of human auditory equilibrium, we have developed an effective scheme using a group of strategical reference markers on the bridge girders to measure structural displacements in the bridge. Our approach integrates the phase-based sampling moiré technique with four degrees-of-freedom geometric modeling to accurately delineate the desired bridge displacements from camera motion-induced displacements. The proposed scheme demonstrates favorable precision with accuracy reaching up to 1/100th of a pixel. Real-world validations further confirmed the reliability and efficiency of this technique, making it a practical tool for bridge displacement measurement. Beyond its current applications, this methodology holds promise as a foundational element in shaping the landscape of future autonomous infrastructure inspection systems.

The success of deep neural networks in carrying out a wide variety of cognitive tasks also raised expectations regarding the advent of AI for the ultrasonic testing (UT) data interpretation in the Non-destructive evaluation (NDE) field. Though it is a growing area of research, we identify two main barriers that hinder research in the field: the lack of real-world, annotated datasets accessible to the public and the scarceness of benchmarked performance of the state-of-the-art deep learning models. To address these issues, we first introduce a new dataset called “USimgAIST” which contains more than 7000 real ultrasonic inspection images with both normal cases and defective ones from 17 types of flaws. Using the dataset, we performed a comprehensive evaluation of representative deep learning models. Through the study, we expect to validate whether existing AI models can achieve human-level ultrasonic image understanding for defect characterization. Besides, all detailed benchmarking comparisons, including defect detection accuracy, model complexity, memory usage, and inference time, are shown. We hope this study exhibits an overview of performances of advanced learning models working for ultrasonic image analysis and lays the groundwork for prospective practitioners to compare their methods and results fairly.

Classification of environmental sounds is a fundamental procedure for a wide range of real-world applications. In this paper, we propose a novel acoustic feature extraction method for classifying the environmental sounds. The proposed method is motivated from the image processing technique, local binary pattern (LBP), and works on a spectrogram which forms two-dimensional (time-frequency) data like an image. Since the spectrogram contains noisy pixel values, for improving classification performance, it is crucial to extract the features which are robust to the fluctuations in pixel values. We effectively incorporate the local statistics, mean and standard deviation on local pixels, to establish robust LBP. In addition, we provide the technique of L ₂ -Hellinger normalization which is efficiently applied to the proposed features so as to further enhance the discriminative power while increasing the robustness. In the experiments on environmental sound classification using RWCP dataset that contains 105 sound categories, the proposed method produces the superior performance (98.62%) compared to the other methods, exhibiting significant improvements over the standard LBP method as well as robustness to noise and low computation time.

Ultrasonic wave propagation imaging enables the detection of anomalies in various structures; hence, it has been applied as one of the promising techniques for damage identification in structural health monitoring (SHM). The interpretation of imaging data is vital to SHM; however, it relies significantly on expert subjective judgment, rendering the results vulnerable to human errors. Recent advances in the field of computer vision arising from the adoption of deep neural networks have resulted in new perspectives for substituting human roles in laborious data interpretation tasks. This paper presents an effective learning architecture that can characterize the ultrasonic wave propagation videos for automatic non-destructive inspection. The main contribution is threefold: 1. To the best of our knowledge, this is the first study to leverage video content analysis techniques to exploit ultrasonic wave propagation image series. Previous approaches that focused on the still wavefield images are likely to lose critical temporal information, thereby resulting in an inferior performance. 2. We devise a model that progressively aggregates both temporal and spatial information encoded in multiple adjacent snapshots of ultrasonic wave propagation motions for efficient data analysis. We presented the details regarding the system implementation and critical parameter settings. 3. The proposed approach is validated through extensive experimental comparisons with other state-of-the-art computer vision techniques on a real dataset which is publicly available. We hope that this study will encourage further investigations into video-based non-destructive data interpretation, not limited to ultrasonic signals.