The detection of rail surface defects is vital for high-speed rail maintenance and management. The CNN-based computer vision approach has been proved to be a strong detection tool widely used in various industrial scenarios. However, the CNN-based detection models are diverse from each other in performance, and most of them require sufficient training samples to achieve high detection performance. Selecting an appropriate model and tuning it with insufficient annotated rail defect images is time-consuming and tedious. To overcome this challenge, motivated by ensemble learning that uses multiple learning algorithms to obtain better predictive performance, we develop an ensemble framework for industrialized rail defect detection. We apply multiple backbone networks individually to obtain features, and mix them in a binary format to obtain better and more diverse sub-networks. Image augmentation and feature augmentation operations are randomly applied to further make the model more diverse. A shared feature pyramid network is adopted to reduce model parameters as well as computation cost. Experimental results substantiate that the approach outperforms single detecting architecture in our specified rail defect task. On the collected dataset with 8 defect classes, our algorithm achieves 7.4% higher mAP.5 compared with YOLOv5 and 2.8% higher mAP.5 compared with Faster R-CNN.

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Image-based rail defect detection could be conceptually defined as an object detection task in computer vision. However, unlike academic object detection tasks, this practical industrial application suffers from two unique challenges, including object ambiguity and insufficient annotations. To overcome these challenges, we introduce the pixel-wise attention mechanism to fully exploit features of annotated defects, and develop a feature augmentation framework to tackle the defect detection problem. The pixel-wise attention is conducted through a learnable pixel-level similarity between input and support features to obtain augmented features. These augmented features contain co-existing information from input images and multi-class support defects. The final output features are augmented and refined by support features, thus endowing the model to distinguish between ambiguous defect patterns based on insufficient annotated samples. Experiments on the rail defect dataset demonstrate that feature augmentation can help balance the sensitivity and robustness of the model. On our collected dataset with eight defected classes, our algorithm achieves 11.32% higher mAP@.5 compared with original YOLOv5 and 4.27% higher mAP@.5 compared with Faster R-CNN.

The problem of foreign object intrusion onto the track bed often occurs in the actual operation process of high-speed railways. To solve the problem, we propose an anomaly detection method for the ballastless track bed, which is based on semantic segmentation. Firstly, we put forward the RFODLab semantic segmentation network according to the randomness of foreign objects distribution, and a small proportion of target pixels in the track image. The segmentation results of track image obtained through this model can be used to obtain the accurate pixel information of foreign objects. To further improve the recall and precision, the channel attention mechanism is introduced for the backbone network of the model to aggregate the context information of images, which achieves the weighted constraints of the model on the area to be recognized. Furthermore, to improve the model performance affected by unbalanced sample category distribution during the anomaly detection, we modify the loss function by balancing distribution of each category. The experimental results show that our proposed method can effectively segment various types of anomalies on the ballastless track bed including broken elastic strips, animal carcasses, and fallen pieces. The precision of anomaly detection on the test set can reach 90% while the recall can be maintained at more than 95%. The anomaly detection results on actual lines also verify the effectiveness of the method.