Tunnel lining quality detection based on the YOLO-LD algorithm

Abstract

Tunnel lining defects, such as discontinuous reinforcing steel, concrete dehollowing, and incomplete pouring, can substantially undermine structural durability and stability. Addressing limitations such as strong subjectivity and low accuracy in traditional quality assessment methods, we introduce the YOLO-LD tunnel lining quality detection algorithm. This model is an adaptation of the original YOLOv7 algorithm, where the original feature pyramid network is substituted by an asymptotic feature pyramid network, and a convolutional block attention module is added subsequently to backbone extraction. Electromagnetic wave propagation is simulated using gprMax3.0, while the tunnel lining structure is imaged through ground-penetrating radar employing the finite-difference time-domain method. These simulations yield a comprehensive radar image dataset for tunnel lining quality evaluation. A performance comparison of YOLO-LD with four other established algorithms reveals its superiority in detecting the three aforementioned defects, yielding an mF1 score of 91.07 % and a mAP score of 94.13 %. The model demonstrates robust performance in comprehensive defect detection and generalization.