UAV aerial image-based apparent defect detection method for embankments using an integrated framework of improved YOLOv8 and SAHI

To address the challenges of detecting apparent structural defects like cracks in large-scale embankment, an unmanned aerial vehicle (UAV) aerial image-based apparent defect detection method is proposed using UAV aerial photography and computer vision technologies. Firstly, Cycle-Consistent Adversarial Networks (CycleGAN) is employed to generate virtual apparent defect images under different lighting conditions using the limited aerial images via data augmentation, and then an image sample dataset of apparent structural defects is established. Subsequently, Convolutional Block Attention Module (CBAM) is utilized to enhance the feature extraction capability of You Only Look Once version 8 (YOLOv8) network, and Scylla-Intersection over Union (SIoU) is employed as the localization loss function to further improve the training efficiency and detection accuracy. Thereby, an improved YOLOv8 network-based intelligent detection model is established for identifying the apparent defects in embankment. Furthermore, Slice-aided Hyper Inference (SAHI) module is inserted into the improved YOLOv8 network to promote the small target detection capacity from long-distance high-resolution UAV images during inference period, and accordingly, an integrated framework of improved YOLOv8 and SAHI is proposed for apparent defect detection of embankment. Engineering example shows that, compared with YOLOv8 network, the improved YOLOv8 network exhibits higher detection accuracy and lower missed detection rate. Meanwhile, the image sample dataset augmented by virtual images is beneficial for improving the training ability, and the utilization of SAHI significantly promotes the inference performance of the intelligent detection model. A high-precision intelligent apparent defect detection approach is provided for safety management and danger inspection of embankment engineering.