This study proposes an enhanced detection method for tunnel lining hidden objects based on the comprehensive Ground Penetrating Radar (GPR) dataset and an improved deep learning (DL) algorithm. To mitigate the issue of data scarcity, a tailored dataset consisting of six types of hidden objects was compiled, which contained 7426 on-site collected GPR images and 71,014 well-labeled objects. This bespoke dataset offered substantial support for algorithm training and validation, making it one of the most extensive datasets in the domain for tunnel lining GPR detection. An enhanced Faster R-CNN algorithm was then developed to effectively detect different types of hidden objects. Modifications were made in four key areas: Feature extraction module, Feature Pyramid Network (FPN), customized anchor scheme and modified loss function. This algorithm was subsequently trained and validated using the comprehensive dataset. Evaluation metrics demonstrated the performance of this approach, with a mean average Precision (mAP) of 81.2%. Results also proved particularly effective in detecting rebars, pipelines, and steel arches while demonstrating a shortfall in detecting voids and cavities due to the data limitation. The results underscore the potential of this enhanced algorithm for the automatic detection of diverse hidden objects in tunnel linings, thus stimulating further research in this vital field.