Two Deep Learning Networks for Rail Surface Defect Inspection of Limited Samples With Line-Level Label

Abstract

Rail surface defect (RSD) inspection is an essential routine maintenance task. Computer vision testing is suitable for RSD inspection with its intuitiveness and rapidity. Deep learning techniques, which can extract deep semantic features, have been applied to inspect RSDs in recent years. However, these methods demand thousands of samples. And sample collection requires hard-working and costs high. To address the issue, a novel inspection scheme for RSDs is presented for limited samples with a line-level label, which regards defect images as sequence data and classifies pixel lines. Thousands of pixel lines are easy to be collected and labeling line-level is a simple task in labeling works. Then two methods OC-IAN and OC-TD are designed for inspecting express rail defects and common/heavy rail defects, respectively. OC-IAN and OC-TD both employ one-dimensional convolutional neural network (ODCNN) to extract features and long- and short-term memory (LSTM) network to extract context information. The main differences between OC-IAN and OC-TD are that OC-TD applies a double-branch structure and removes the attention module. Experimental results on RSDDs dataset demonstrate that our methods are effective and outperform the state-of-the-art methods on defect-level metrics (Type-I: Rec-0.9314, Pre-0.8421, F1-0.8845; Type-II: Rec-0.9427, Pre-0.9176, F1-0.9300).