UAV-based deep learning applications for automated inspection of civil infrastructure

Modern technologies such as Unmanned Aerial Vehicle (UAV)-based inspection and deep learning (DL) algorithms introduce new opportunities and challenges in Civil Engineering. To better facilitate the adoption and advancement of UAV-based detection technologies, this paper conducts a systematic literature review on a plethora of articles and performs a comprehensive investigation and comparison across four different topics: (1) investigating the technical specifications of currently utilized UAV platforms and of the employed on-board sensors, (2) summarizing the categories of inspected infrastructure and the corresponding defects, (3) collecting publicly available datasets established on infrastructure defects, (4) illustrating and comparing DL algorithms designed for defect detection. Based on the analysis of collected related work, challenges hindering the development of UAV-based infrastructure inspection, solutions, and potential future opportunities are proposed. This review is aimed at assisting researchers and practitioners to accelerate progress toward more efficient and safe autonomous UAV-based structural inspection in civil engineering. • Comprehensive categorization of infrastructure types and their associated defects that can be detected by UAV-based systems. • Systematic analysis of UAV platforms and sensors specifications for infrastructure defect detection. • Collection and categorization of public datasets for training and validation of deep learning-based defect detection models. • Critical comparison of deep learning algorithms designed for automated defect recognition. • Identification of challenges and future opportunities in UAV-based automated inspection.