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Owing to the development of convolutional neural networks (CNNs), the detection of defects on rail surfaces has significantly improved. Although existing methods achieve good results, they incur huge computational and parameter costs associated with CNNs. The usual approach to this problem is to design lightweight models that meet the needs of real-world applications; however, the performance is often compromised. To address the aforementioned problems, we designed a dual semantic approximation network via knowledge distillation (DSANet-KD, a student model with knowledge distillation) for rail surface defect detection; it focuses on both foreground and background knowledge and obtains more accurate prediction results. This model comprises an adaptive 3D spatial integration module, feature-optimization decoding module, and dual semantic approximation knowledge-distillation framework. Specifically, we employed a thoroughly trained teacher defect detection network equipped with dual semantic approximation information as an experienced teacher to guide the training of a student defect detection network. Experimental results showed that the proposed DSANet-KD achieved better accuracy with a smaller number of parameters than the state-of-the-art methods. To demonstrate the generalizability of DSANet-KD, experiments were conducted on a publicly available RGBD-SOD dataset, whose source code is available at: https://github.com/hjklearn/DSANet-KD.

Deep learning techniques have largely solved the problem of rail surface defect detection (SDD), however, two aspects have yet to be addressed. In most existing approaches, two red–green–blue and depth (RGB-D) streams are indiscriminately fused across modalities, ignoring the fact that RGB and depth images produce different feature qualities in different scenes. Additionally, in their focus on performance, previous studies have overlooked the fact that models produce several parameters, resulting in unrealistic practical applications. To address these challenges, we designed a modal evaluation network (MENet) via knowledge distillation (KD) (MENet-S*) for a no-service rail SDD to adaptively manage information in each scenario and achieve model compression. First, to dynamically adjust the feature distribution and quality, dynamic and static feature coding ideas are introduced. Second, modal evaluation distillation is introduced, which allows a compact model (MENet-S) to learn the feature evaluation process of a complex model (MENet-T). Third, to enable MENet-S to learn the dynamic encoding process of MENet-T and to improve the feature representation of MENet-S, we propose accessible knowledge distillation. Furthermore, multitiered KD is introduced to facilitate the learning of MENet-S. Based on extensive experiments using the industrial RGB-D dataset NEU RSDDS-AUG, we observed that MENet-S* (MENet-S with KD) outperformed 16 state-of-the-art methods. In addition, to demonstrate the generalization capability of MENet-S*, we evaluated the proposed network on three additional public datasets, and MENet-S* achieved competitive results. The source codes and results are available at https://github.com/hjklearn/MENet-KD.