UAV-Driven Bridge Crack Boundary Refinement Segmentation Network Based on Rendering Technology

In practical engineering, high-resolution (HR) imaging devices have become increasingly utilized for capturing structural surface crack images. However, the effectiveness of current deep learning (DL) segmentation models in accurately predicting refined masks for HR crack images is hindered by the discrete sampling methods inherent in traditional DL architectures and the limited computational resources of GPUs. To tackle this issue, this investigation incorporates the point-based rendering methodology originating from computer graphics disciplines into the encoding-decoding framework, introducing an innovative Crack Boundary Point Rendering Network (CBPRN). The CBPRN endeavors to accomplish elaborate delineation of crack visual samples possessing resolutions surpassing 4K. Initially, an edge feature extractor integrated with a super-resolution encoder is devised to guide rendering heads in efficiently focusing computational power on ambiguous edge regions. Subsequently, a rendering-based prediction head is introduced with the function of efficiently sampling rendering points for the training and inference phases, respectively. Furthermore, a tailored composite objective function is deployed to enhance the learning procedure, enabling the architecture to equilibrium substantial disparities in pixel counts among positive and negative instances within crack visual data. Ultimately, to substantiate the practical applicability of the CBPRN, an on-site crack identification investigation was executed on an actual bridge structure located in Changsha utilizing an unmanned aerial vehicle (UAV). The CBPRN demonstrated remarkable effectiveness on 4K-resolution visual samples acquired by the unmanned aerial vehicle, attaining achieving overlap ratio (Intersection over Union, IoU), average boundary precision (mean Boundary Accuracy, mBA), and Dice similarity index metrics of 85.46%, 86.00%, and 92.16%, correspondingly. This outstanding effectiveness improves both the operational security and processing efficiency of unmanned aerial vehicle-assisted crack assessment procedures, offering enhanced flexibility in choosing flight trajectories for the inspection workflow.