Microwave nondestructive testing (MNDT) has shown great potential in detecting defects in various materials. This is due to it being safe and noninvasive. Safety is essential for the operators as well as the specimens being tested. Being noninvasive is important in maintaining the health of critical structures and components across various industries. In this paper, a review of MNDT methods is given with a comparison against other NDT techniques. First, the latter techniques are described, namely testing using a dye penetrant, ultrasound, eddy currents, magnetic particles, or radiography. Next, an overview of various microwave NDT methods is provided through a review of the applications, advantages, and limitations of each technique. Further, a detailed review of emerging MNDT techniques like microwave microscopy, active microwave thermography, and chipless radio frequency identification is presented. Next, a brief description of current and emerging algorithms employed in MNDT is discussed, with emphasis on those using artificial intelligence. By providing a comprehensive review, this article aims to shed light on the current state of MNDT, thus serving as a reference for subsequent innovations in this rapidly evolving domain.

This study investigates the application of Ka-band (35 GHz) microwave non-destructive testing (NDT) to detect and classify surface-breaking defects in metallic pipelines and both internal and external defects in non-metallic pipelines. A microwave scanning system utilizing an open-ended rectangular waveguide sensor collects 420 one-dimensional voltage line-scan measurements across six defect types (hole, cut, corrosion, internal wear, external wear, and inclusion) in PVC, HDPE, and metallic pipe specimens at three standoff distances (1 mm, 2 mm, and 3 mm). A one-dimensional convolutional neural network (1D CNN), trained with SMOTE resampling and $4\times$ data augmentation, is evaluated under a grouped protocol in which all six scans of any physical defect specimen are confined to a single partition. The network takes the scan through a convolutional branch and receives the standoff distance and material code as separate auxiliary inputs. Grouped five-fold cross-validation, repeated five times over independent partitions, gives a mean accuracy of 82.19 ± 5.93% with a macro-averaged F1-score of 0.85. Errors are concentrated among hole, cut, and external wear, while corrosion, inclusion, and internal wear are recovered with recall at or above 0.90. Validation used artificially introduced, isolated defects in laboratory specimens under controlled acquisition conditions. These results establish laboratory feasibility for Ka-band microwave NDT combined with a 1D CNN as a noninvasive route for automated pipeline defect classification, and show that a learned classifier resolves signal ambiguity that makes manual interpretation of amplitude-only microwave sensor outputs difficult.