The YOLOv8 model has been shown to offer several advantages in detecting defects on concrete surfaces. However, it is ineffective at achieving multiscale feature extraction and accurate detection of underground structures under complex background conditions. Therefore, this study developed a YOLOv8-PSN model to detect surface defects in underground structures more rapidly and accurately. The model uses PSA (Pyramid Squeeze Attention) and Slim-neck to improve the original YOLOv8. The PSA module is adopted in the backbone and neck network to improve the model’s perception of multiscale features. Meanwhile, a Slim-neck structure is introduced into the Neck part to improve computational efficiency and feature fusion. Then, a dataset comprising six concrete surface defect categories, including cracks and spalling, is built and used to evaluate the performance of the developed YOLOv8-PSN. Experimental results show that, compared with the original YOLOv8, YOLOv10, YOLOv11, SSD, and faster R-CNN, the mAP@50 of YOLOV8-PSN increases by 4.48%, 5.32%, 3.47%,20.03%, and 20.93%, respectively, while still maintaining a high-speed, real-time detection speed of up to 99 FPS. Therefore, the developed model has good robustness and practicality in a complex environment and can effectively and rapidly detect surface defects in underground structures.
Ma et al. (Mon,) studied this question.