A Review of UAV-Based Crack Detection in Civil Infrastructure: A Multi-Level Visual Analysis Framework, Scene Adaptability, and Challenges

Civil infrastructure plays a critical role in ensuring societal safety and economic development. However, structural damages such as cracks inevitably occur during long-term service. Traditional manual inspection methods are insufficient to meet the demands of large-scale and routine monitoring. Unmanned Aerial Vehicles (UAV) remote sensing has become an important approach for Structural Health Monitoring (SHM), owing to its high spatial resolution imaging capability and superior operational flexibility. Nevertheless, existing studies focus on optimizing individual algorithms, lacking a systematic analysis oriented toward multi-scenario engineering applications. Therefore, we present a comprehensive review of UAV-based crack detection techniques for infrastructure using remote sensing imagery. First, publicly available datasets, UAV platforms, and evaluation metrics are systematically summarized. Then a multi-level visual analysis framework for UAV inspection is established. The framework categorizes existing methodologies into five levels: image-level classification, object-level detection, pixel-level segmentation, geometric quantification, and three-dimensional (3D) reconstruction, followed by a systematic evaluation of representative methods. Furthermore, the applicability of different methods across diverse scenarios, including bridges, pavements, dams, building facades and wind turbine blades, is systematically explored. Finally, the key challenges and future research directions are discussed. This review aims to provide a systematic theoretical foundation and methodological reference for advancing UAV-based infrastructure crack inspection from algorithm development toward practical multi-scenario engineering applications.