Decision-Level Fusion of Spatially Scattered Multi-Modal Data for Nondestructive Inspection of Surface Defects

Abstract

This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique’s robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate.

Spatiotemporal Fusion in Remote Sensing

“…The decision-level fusion operates on a product level, where it requires images to be fully and independently processed until the meaningful output is obtained (e.g., classification or change detection maps) (see Figure 5). Decision-level fusion can adapt to different modularities like combing heterogeneous data such as satellite and depth images, which can be processed to common outputs (e.g., full/partial classification maps) for fusion [88]. Additionally, the techniques followed by this fusion type are often performed under the umbrella of Boolean or statistical operations using methods like likelihood estimation, voting (e.g., majority voting, Dempster-Shafer's estimation, fuzzy Logic, weighted sum, etc.)…”

“…Additionally, the techniques followed by this fusion type are often performed under the umbrella of Boolean or statistical operations using methods like likelihood estimation, voting (e.g., majority voting, Dempster-Shafer's estimation, fuzzy Logic, weighted sum, etc.) [88][89][90]. In [88], they provide an example on the mechanism of decision-level fusion; they developed a fusion approach to detect cracks and defects on ground surface, they first convert multitemporal images into spatial density maps using kernel density estimation (KDE), then, fused the pixels density values using a likelihood estimation method.…”

Data fusion approaches for structural health monitoring and system identification: Past, present, and future

“…Numerical and experimental results indicate a good agreement between the estimated dense displacements and the measured displacements. In Heideklang and Shokouhi, 203 a kernel density estimation-based approach 204 Tables 10 and 11 summarize the above applications using weighting approaches in damage identification/quantification and system identification/response estimation, respectively. The information of input data source, validation approach, and limitations/concerns are presented.…”

“…measurements P Performance may depend on environmental factors, for example, illumination condition. Heideklang and Shokouhi 203 Images from eddy current test, magnetic flux leakage, and thermography test P Performance depends on fusion rules.…”

Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

“…As a result of the high performance of the multi-sensor data fusion method on the noise elimination to the process control application [25], it was chosen in order to analyze the control process of a bulk tobacco curing schedule in this study. The multi-sensor data fusion method has been widely used in various research areas [17,26,27,28,29,30,31]. In the field of artificial sensors’ applications, which are highly related to the present study, a feature level fusion with principal component analysis (PCA) feature selection method and several pattern analysis techniques, such as ANN, linear discriminant analysis (LDA), partial least square (PLS), and support vector machine (SVM), have been mostly used for food authentication and the on-line monitoring of food fermentation processes [30,32,33,34].…”