In order to ensure the safety of rail transit, detecting the flaws on the rail surface is vitally important. Instead of present manual inspections, detecting defects on rail surface by an automatic approach enables the work more efficient and safe currently. In this paper, we propose a novel two-stage pipeline method for defect detection on rail surface by localizing rails and sliding a deep convolutional neural network (DCNN) on rail surface. Specifically, in the first stage, we use an anchor-free detector to locate the tracks in original images and get the cropped images which focus on rail part. In the second stage, a trained deep convolutional neural network slide on the cropped images to detect defects and we can finally get the types and approximate locations of the defects on rail surface. The experimental results show that the proposed method has robustness and achieves practical performance in defect detection precision.
Fasteners are the important components of railway system, which can be used to fix the tracks to the sleepers and reduce the likelihood of derailment. Nowadays, the extensively used approaches for the automatic detection of defective fasteners are vision-based approaches. However, they are not robust and efficient enough to be applied in reality. To solve this problem, this paper applies deep convolutional networks for the automatic detection of fastener defect and proposes a two-stage fastener defect detection framework. The framework is composed of a CenterNet-based fastener localization module and a VGG-based defect classification module. Besides,we innovatively introduce an attention mechanism named CBAM into localization network and an adaptive weighted softmax loss in classification network training procedure to elevate the accuracy of both modules. The experiment result shows that both methods have obviously improved the performance of the fastener defect detection system. The proposed localization network has a better accuracy-speed trade-off with 99.94% AP at 63 FPS on the test set. In addition, the proposed defect classification network has the best accuracy (up to 98.10%) on the test set and can be used to classify up to 5 categories of defects.
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods. Especially, our method has been favorably evaluated in terms of noise suppression and structural preservation.