ABSTRACT Corrosion remains a critical threat to the integrity and service life of infrastructure in industries such as oil, gas, construction, renewable energy, and transportation. Traditional inspection methods, being labor‐intensive, hazardous, and often subjective, fall short in addressing modern inspection demands. This review presents an overview of drone‐based corrosion detection technologies, highlighting their potential to transform inspection practices through improved safety, efficiency, and cost‐effectiveness. Drones equipped with sensors like high‐resolution RGB cameras, thermal cameras, and hyperspectral systems can be used to closely inspect corrosion, especially in areas that are difficult or unsafe for humans to reach. The article examines key methodologies including deep learning, computer vision, and spectroscopic techniques, and discusses their role in enhancing detection accuracy and automation. Applications across various sectors are explored, emphasizing the growing relevance of drones in monitoring pipelines, bridges, and renewable energy systems. Particular focus is placed on detecting hidden corrosion in galvanized steel, which requires specialized sensors and AI models that can pick up subtle changes in the material. The integration of multi‐sensor drones with ground “on‐site” validation techniques is also highlighted as a critical strategy for improving accuracy and reliability. While notable progress has been made, challenges such as environmental variability, data quality issues, and AI model interpretability persist. This review identifies existing research gaps and proposes future directions, including the integration of artificial intelligence, digital twins, and multi‐sensor data fusion, to further advance the reliability and effectiveness of drone‐based corrosion detection systems.
Alharbi et al. (Sat,) studied this question.