In cold environments, ice formation poses significant risks to infrastructure such as transportation systems and power transmission. Yet, traditional de-icing methods are often time-consuming, hazardous, and inefficient. In this regard, unmanned aerial vehicles (UAVs) have shown great potential in environmental ice detection and de-icing applications. This study comprehensively reviews the application of UAVs in ice detection and de-icing operations in external environments, emphasizing their potential to replace traditional manual methods. Firstly, the latest developments in UAV-based external ice detection technology are examined, with a focus on the unique capabilities of sensors such as multispectral cameras, infrared imagers, and LiDAR in capturing specific ice features. Subsequently, the implementation and effectiveness of chemical, mechanical, and thermal de-icing methods delivered via UAV platforms are evaluated, focusing on their operational efficiency and adaptability. In addition, key operational requirements are reviewed, including environmental adaptability, mission planning and execution, and command transmission, as well as system design and manufacturing. Finally, the practical challenges involved in deploying UAVs under complex weather conditions are examined and solutions are proposed. These are aimed at promoting future research and ultimately driving the adoption of UAV technology in de-icing applications.

Active infrared thermography (IRT) has been extensively employed in non-destructive testing in a wide variety of fields. It is capable of extracting defect information of tested object based on the infrared thermal image sequence. However, conventional infrared thermal images are often subjected to defect information with low pixel resolution, and defects are difficult to quantitatively analyze. By exploiting flat-bottomed holes in a PVC plate as defect specimens, a method for quantitative defect depth recognition based on the fusion principal component analysis (PCA) algorithm with sliding-window mechanism and the one dimensional—residual neural network—convolutional block attention module (1DResnet50_CBAM) model was proposed for the reconstructed image sequence of active IRT to address the above-described issues in this study. First, defect information and location were extracted from the original infrared sequence thermal image of the specimen using PCA algorithm with sliding-window mechanism. Then, the dimension of the defect data was reduced using the temporal characteristic of the infrared temperature field. That is, the three-dimensional defect data were transformed into 1D temporal infrared thermal signal. Moreover, the 1D infrared signal time series corresponding to the defect pixel points in the infrared sequence image served as the input to the network, and the defect depth served as the output for automatic defect detection and depth quantification. As indicated by the results, the proposed method based on the fusion PCA algorithm with sliding-window mechanism and 1DResnet50_CBAM model is capable of accurately detecting and quantifying defects. Compared with conventional prediction algorithms, the proposed model can more effectively extract defect information from the infrared detection images, with the defect depth relative prediction error less than 1.5%. Thus, the proposed model was confirmed as an effective method and model for defect recognition and quantitative analysis using infrared thermal detection technology.