Tunnel Lining Crack Intelligent Recognition Based on YOLOv11 Algorithm

<div class="section abstract"><div class="htmlview paragraph">Tunnel linings are an important safeguard for the integrity and stability of tunnels. However, cracks in the tunnel lining may have extremely unfavourable consequences. With the acceleration of urbanisation and the increasing construction of tunnels, the problem of cracks in the concrete lining is becoming more and more prominent. These cracks not only seriously affect the stability of the structure, but also pose a serious threat to the safety of tunnel operation. If left unchecked, the cracks may expand further and cause various safety hazards, such as water leakage and falling blocks. This in turn will undermine the normal function of the tunnel and endanger the lives of tunnel users. It has been proved that the traditional manual method of detecting cracks in tunnels has problems such as low accuracy and low efficiency. In order to solve this problem, it is very necessary for this study to pioneer an intelligent method for identifying tunnel lining cracks using the YOLOv11 algorithm. A unique crack dataset was constructed for model training and testing. The experimental results are very encouraging, with an accuracy of 93.3% for the evaluation metrics, a recall of 94.5% and an average precision of 96.9%. This innovative method shows good accuracy and practicality in tunnel lining crack detection, and its innovation lies in adopting advanced algorithms to locate cracks efficiently and accurately, breaking through the traditional detection limitations. It can help engineers and technicians to identify and evaluate tunnel lining cracks more effectively and take timely repair and maintenance measures, which is conducive to improving the safety and service life of tunnels.</div></div>