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Periodic manual inspection by trained specialists is an important element of asset management in the nuclear industry. Detection of cracks caused by stress corrosion is an important element of remote visual inspection (RVI) in power plant steam generator components such as boilers, superheaters and reheaters. Challenges exist in the interpretation of RVI footage, such as high degree of concentration for reviewing lengthy and disorienting footage due to narrow field of view offered by endoscope. Deep learning is considered useful to automate crack detection process for improved efficiency and accuracy, and has enjoyed success in related applications. This article utilises a new application of automated crack feature detection in steam cycle components to demonstrate a transferrable data-driven framework for a variety of anomaly inspections in such structures. Specifically, a case study of superheater (a type of reactor pressure vessel head) anomaly inspection is presented to automatically detect regions of crack-like features in inspection footage with a good accuracy of 92.97 % using convolutional neural network (CNN), even in challenging cases. Due to the black-box nature of the CNN classification, the explicability of the classification results is discussed to enhance the trustworthiness of the detection system. • Deep learning is useful to support remote visual inspection of pressure vessels at nuclear power plants. • Automated anomaly detection in steam cycle components is showcased through crack-feature inspection in superheaters. • The presented data-driven framework is transferrable for various anomaly inspections in pressure vessel structures. [ABSTRACT FROM AUTHOR]