RDNet-KD: Recursive Encoder, Bimodal Screening Fusion, and Knowledge Distillation Network for Rail Defect Detection

Rail defect detection (RDD) plays a crucial role in ensuring rail transportation safety. Recently, bimodal algorithms have become mainstream; however, the asymmetry in the information of RGB and depth makes it difficult to find a suitable bimodal information fusion algorithm. In addition, it is difficult to deploy most of the existing methods on mobile devices. To solve these problems, we propose a recursive encoder and bimodal information screening fusion with a knowledge distillation network (RDNet-KD) for RDD. First, we propose the recursive encoder-based depth information augmentation (REDA) algorithm. It recursively learns to expand the channel depth information to alleviate the quality problem of depth information. Second, we propose a similarity-driven bimodal information screening fusion (SICF) module. This evaluates the complementarity of information from two modalities by computing the similarity of their hierarchical feature maps to screen useful information for fusion. Third, we introduce the global location and interrelation-based dual contextual knowledge distillation method to enhance the performance of the compact model. Therefore, it is possible to deploy the network on mobile devices. Based on the extensive experiments performed on the RGB-D rail defect dataset NEU RSDDS-AUG, we validate the competitiveness of our RDNet-KD, considering the prediction quality and operational efficiency relative to 12 state-of-the-art methods. The RDNet-KD code and results are available at https://github.com/legendfantasy/RDNet-KD.
Note to Practitioners
—This study introduces a recursive encoder and bimodal information screening fusion with a knowledge distillation network (RDNet-KD) for RDD in RGB-D images. Our method enhances depth information quality and effectively selects valuable information from both modalities using the similarity as a coefficient to evaluate the complementary capabilities of the modal information. Furthermore, to compress the model, we introduce knowledge distillation (KD) to balance the number of parameters and detection results and propose a novel KD method that transfers knowledge from the teacher network to the student network.