Intelligent tunnel defect detection is essential for structural safety and efficient operation and maintenance. However, manual inspection is inefficient, subjective, and risky, while existing deep learning methods often show unstable performance under practical conditions involving small targets, large-scale variations, and severe background interference, limiting their accuracy and real-time deployment on edge devices. To address these issues, this paper proposes CAMD-RTDETR, an end-to-end real-time multi-defect detection method based on RT-DETR. Cross-attention feature mining is introduced to enable bidirectional interaction between shallow spatial details and deep semantic information, enhancing the perception of weak-texture defects such as fine cracks. Multi-scale contextual pooling is designed to aggregate features from different receptive fields and improve the unified representation of cracks, seepage, and spalling with diverse morphologies. In addition, decoding enhancement and query optimization are incorporated to improve query updating and localization discrimination, thereby enhancing detection stability and boundary accuracy in complex tunnel scenes. Experiments on a field-collected tunnel defect dataset show that CAMD-RTDETR achieves an average inference latency of 15.855 ms per image and a processing speed of 63.06 FPS under the batch-size-1 testing setting. Compared with the baseline RT-DETR, Precision, Recall, mAP50, and mAP50-95 are improved by 6.3%, 13.5%, 14.7%, and 15.8%, respectively. Comparisons with seven representative detectors further demonstrate its superior accuracy and real-time performance, demonstrating its preliminary feasibility for edge-side inference and its potential for future integration into vehicle-mounted tunnel inspection systems.
Hao et al. (Mon,) studied this question.