DSANet-KD: Dual Semantic Approximation Network via Knowledge Distillation for Rail Surface Defect Detection

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: <uri>https://github.com/hjklearn/DSANet-KD</uri>.