## 1 Introduction  
Industrial nondestructive testing (NDT) refers to the inspection of materials, parts or structures concerning their condition without compromising their structural integrity. NDT experts employ different sensors depending on the material and the anticipated types of defects. Near-surface cracks represent one class of flaws that are commonly encountered in critical machine parts under dynamic loading such as turbine blades and bearings. Repeating cycles of dynamic stress promote the growth of invisible micro cracks into larger cracks that shorten the service life of the defective part. As such, early diagnosis of faults is critical to the operational safety and serviceability of a broad range of machinery. Among the NDT methods suitable for near-surface crack detection, special attention is paid to those that allow automatic data acquisition and provide accurate, quantitative and reproducible results in the form of digitized signals. Common candidate methods are ultrasonic testing (UT), eddy current testing (ET), magnetic flux leakage (MFL) testing, and thermal testing (TT). However, single-method inspection is often ambiguous. This is because each method reacts to changes in specific physical properties of the tested structure in the presence of a defect, but the intrinsic material and geometry properties of the structure may also produce similar and often indistinguishable changes in the recorded signals. For instance, MFL is affected by surface roughness and inhomogeneous magnetic properties, and TT's high sensitivity to negligible manufacturing and handling marks and imperfections results in a multitude of false positive indications. These non-defect indications are referred to as structural noise, because unlike measurement noise, they are deterministic across repeated measurements. Yet, structural noise varies across different inspection techniques, whereas we hypothesize that actual flaws should be characterized by multi-sensor agreement. The underlying assumption is that all NDT sensors are in principle sensitive to the same class of defects, for example surface cracks in a certain parameter range with regards to their position, size and orientation. This concept lies at the heart of NDT data fusion. In other words, multi-sensor inspection differentiates flaw indications from structural noise, thus providing more reliable assessments. Sensor fusion can be performed at various levels of signal abstraction [1], each with specific drawbacks and advantages. In particular, decision-level fusion deals with the higher level aggregation of data after individual detection. That is, each signal is first processed individually, and then fed into the fusion algorithm. We argue that decision-level fusion has several advantages over signal-level fusion for NDT inspection. First, unlike signal-level fusion, the data to be fused do not have to be interpolated at a common grid of positions. Although a spatial association step is still necessary to prepare individual detections for fusion, decision-level fusion can work on the un-interpolated signal intensities. As we will demonstrate, this approach allows for explicit handling of localization uncertainty, for instance due to registration errors. This is in contrast to signal-level fusion, where accurate registration is crucial [2; 3] because errors can hardly be compensated. Strongly localized features, such as cracks, are particularly prone to misregistration and are therefore better handled at a higher level of signal abstraction. A further major benefit is the strategy's modularity. The individual data collection and processing can be carried out independently by the respective experts to tailor the detection process specifically to each inspection method. One practical benefit of the modularity offered by decision-level fusion is that it allows combining individual results, even if fusion was not envisioned in the original testing plan.