Leveraging Multisource Label Learning for Underground Object Recognition

Currently, numerous deep learning (DL) methods have been proposed for the recognition of ground penetrating radar (GPR) B-scan images. Due to the sensitivity of GPR imaging to local underground conditions, DL models trained in other underground environment areas are likely to fail in new areas. Consequently, organizing and labeling new GPR images to train models have become a widely adopted approach in practical applications. However, expert annotation is costly, making it often difficult to collect large-scale datasets with high-quality annotations. Some studies have attempted to improve the quality of image annotations by integrating the efforts of multiple annotators. This process can be constrained by the varying levels of expertise and attention spans of the annotators, which may lead to the presence of errors or contradictions in the provided labels. To address these challenges, this article proposes a method for underground target recognition aimed at multisource annotation tasks. A probability multisource label aggregation (PMLA) module is designed to estimate the reliability of multisource labels, and a label-sensitive regularization (LSR) module is introduced to mitigate the negative impact of potentially erroneous labels on model training. Extensive experiments are conducted on multiple GPR B-scan datasets. The experimental results demonstrate the advantages of the proposed method in handling annotation conflicts and improving the accuracy of underground target recognition.