Optimising signal quality for corrosion detection using the innovative Mag_FSRCNN model to minimise hall sensor array in eddy current testing

The security of aviation hinges significantly on the strength of aircraft structures, with potential risks arising from issues like corrosion impacting the framework. Eddy current testing (ECT) sensors, particularly those utilising array technology, present a promising solution to address this concern. However, challenges persist in improving the clarity of corrosion signals while optimising associated expenses. In response to this, our study introduces the Mag_FSRCNN model, a novel framework that integrates a Fast Super-Resolution Convolutional Neural Network (FSRCNN) module network with a part of Generator module of Generative Adversarial Network to enhance magnetic images obtained by the ECT sensor array. Mag_FSRCNN surpasses traditional methods, showcasing high-resolution capabilities in enhancing ECT signal images. The model demonstrates excellence when evaluated using metrics such as Structural Similarity Index, Root Mean Square Error, and Peak Signal-to-Noise Ratio. These results significantly contribute to the ongoing progress of ECT sensors, facilitating improved resolution and cost-effective application of Hall sensor arrays in the current evolution of ECT sensor technology.