  Material defects (MD) represent a primary challenge affecting product
performance and giving rise to safety issues in related products. The rapid and
accurate identification and localization of MD constitute crucial research
endeavours in addressing contemporary challenges associated with MD. Although
conventional non-destructive testing methods such as ultrasonic and X-ray
approaches have mitigated issues related to low efficiency in manual
inspections, they struggle to meet the diverse requirements of high precision,
real-time speed, automation, and intelligence. In recent years, propelled by
the swift advancement of machine learning (ML) technologies, particularly
exemplified by deep learning, ML has swiftly emerged as the core technology and
a prominent research direction for material defect detection (MDD). Through a
comprehensive review of the latest literature, we systematically survey the ML
techniques applied in MDD into five categories: unsupervised learning,
supervised learning, semi-supervised learning, reinforcement learning, and
generative learning. We provide a detailed analysis of the main principles and
techniques used, together with the advantages and potential challenges
associated with these techniques. Furthermore, the survey focuses on the
techniques for defect detection in composite materials, which are important
types of materials enjoying increasingly wide application in various industries
such as aerospace, automotive, construction, and renewable energy. Finally, the
survey explores potential future directions in MDD utilizing ML technologies.
This comprehensive survey not only consolidates existing literature on ML-based
MDD technologies but also serves as a foundational reference for future
researchers and industrial practitioners, providing valuable insights and
guidance in developing advanced and efficient MDD systems.
