Efficient real-time defect detection for spillway tunnel using deep learning

A spillway tunnel eroded by high-speed water for a long time is prone to the rebar-exposed defects. Therefore, regular defect detection is very important for the safety of the hydropower station. The images of spillway tunnel are obtained by erecting scaffolding, and then the defects are manually recognized. This traditional method has some disadvantages such as high risk, inefficiently, time consumption and strong subjectivity. To improve the efficiency of defect detection, a real-time method is proposed for spillway tunnel defect detection (STDD) using deep learning. First, images of a spillway tunnel are collected by an Unmanned Aerial Vehicle (UAV) system and raw images are cropped and labeled to create a dataset of rebar-exposed defects. Then, the lightweight STDD network is developed using separable convolution and asymmetric convolution, and the network is trained and tested on the dataset. To evaluate the performance of STDD network, a comparative experiment is conducted with other networks. The results show that the STDD network has better detection performance. For defect segmentation, the recall, precision, F1 and mean intersection over union (mIoU) are 89.92%, 93.48%, 91.59%, and 91.73%, respectively. The STDD network has 1.7 M parameters, and the average inference time is 14.08 ms. In summary, the proposed STDD network achieves accurate and real-time defect detection for spillway tunnel, which can provide reliable support for the structure safety evaluation.

Automated summary

The paper introduces a method of using deep learning for spillway tunnel defect detection, which shows significant improvement in efficiency and accuracy compared to traditional manual recognition methods. By collecting images of spillway tunnel using a UAV system and training with a lightweight STDD network, accurate recognition of rebar-exposed defects is achieved.