| description abstract | Deep learning can assist ground penetrating radar (GPR) to accurately identify the internal defects of airport track structure. However, deep learning often requires many annotated samples. For this purpose, a deep active learning (DAL) method with multiple selection criteria has been proposed for defect detection. In addition, a color data set which found data annotation easier in this case, was created, and the model trained with color images improved performance by about 1% compared with the model trained with the original grayscale images. Subsequently, the selection strategies based on entropy, least confidence, and a combined criterion were proposed as an active learning mechanism. The experimental results indicated that when 443 training images were used, the model using the least confidence-based selection strategy achieved a mean average precision (mAP) of 87.50%, and the model using the combined criteria-based selection strategy achieved a mAP of 87.56%. However, the detection model using entropy-based selection strategy used 393 training images to achieve a mAP of 88.06%, which was superior to the other two selection strategies, and the number of training samples was only 53% of that of the initial model. Overall, DAL could achieve similar model performance to traditional deep learning while reducing annotation costs by 47%. This method has a detection speed of 96 frames per second, which meets the requirements of engineering applications for detecting defects in airport pavements. | |
