Lightweight Scope Integration Network for Rail Surface Defect Detection

Abstract

Rail-surface defect detection (RSDD) is a key technology for ensuring the safety and efficiency of railroad transportation. Existing models enhance the robustness in complex scenarios by using complementary information from visible light (RGB) image and depth data. However, incorporating depth data increases the computational complexity, which consumes more resources, increases the risk of overfitting, and decreases the model efficiency. Optimizing and streamlining multimodal data processing remains challenging. To address this issue and enable fast and accurate RSDD, this study introduces a novel lightweight scope integration network (LSINet). This method efficiently and precisely fuses the features using a lightweight double-context-aware module and refines the image quality using an adaptive Markov field smoothing module in a hierarchical decoding process. A lightweight two-dimensional scanning method that captures long-range dependencies and improves computational efficiency. We integrate this approach with local range extremes to enhance the multimodal feature fusion. Furthermore, to refine the defect edges and improve the detection accuracy, we integrate the Markov random field module into the defect segmentation and optimization using its ability to model interpixel spatial correlation, particularly for small and ambiguous defects. In designing the model, we focused on optimizing the number of parameters (e.g., computational resources). Experimental evaluations using various rail surface images demonstrated that the proposed method enhanced the defect detection accuracy and recall while reaching fast detection speeds. According to extensive experiments conducted using the industrial RGB-D dataset (i.e., NEU RSDDS-AUG), LSINet outperformed 15 state-of-the-art methods with only 27.6 million parameters.