An intelligent railway surveillance framework based on recognition of object and railway track using deep learning

Abstract

In high speed railways, the intelligent railway safety system is necessary to avoid the accidents due to collision between trains and obstacles on the railway track. The unceasing research work is being performed to reinforce the railway safety and to diminish the accident rates. The rapid development in the field of deep learning has prompted new research opportunities in this area. In this paper, a novel and efficient approach is proposed to recognize the objects (obstacles) on the railway track ahead the train using deep classifier network. The 2-D Singular Spectrum Analysis (SSA) is utilized as decomposition tool that decomposes the image in useful components. That component is further applied to the deep classifier network. The obstacle recognition performance is enhanced by the combination of 2D-SSA and deep network. This method also presents a novel measure to identify the railway tracks. In addition, the performance of this approach is analyzed under different illumination conditions using OSU thermal pedestrian benchmark database. This system can be a tremendous support to curtail rail accidental rate and monetary loads. The results of proposed approach present good accuracy as well as can effectively recognize the objects (obstacles) on the railway track which helps to the railway safety. It also achieves a better performance with 85.2% accuracy, 84.5% precision and 88.6% recall.

Data augmentation using generative models for track intrusion detection

“…Furthermore, Pan et al 3 explored the application of convolutional neural networks and multi-task learning techniques for this purpose. In Kapoor et al, 4 faster R-CNN is used as a detection method. Additionally, Chen et al 5 proposed an algorithm that utilizes a two-stage network, initially recognizing railway tracks and subsequently detecting objects.…”

“…Recently, studies have been conducted to improve the accuracy and processing speed of these systems by applying deep learning-based algorithms. 1–5…”

“…Recently, studies have been conducted to improve the accuracy and processing speed of these systems by applying deep learning-based algorithms. [1][2][3][4][5] Artificial intelligence (AI) is a system in which computers infer, learn, and judge on their own by learning the rules and patterns of data. Therefore, it is essential for deep learning-based models, a type of AI technology, to secure a sufficient amount of data to achieve satisfactory performance.…”

“…Therefore, there has been significant research conducted on deep learning algorithms for detecting arbitrary intrusions in restricted areas of railway facilities. [1][2][3][4][5] 4 faster R-CNN is used as a detection method. Additionally, Chen et al 5 proposed an algorithm that utilizes a two-stage network, initially recognizing railway tracks and subsequently detecting objects.…”

Intelligent Identification of Reservoir Fluid in Daniudi Gas Field Based on AdaBoost Machine Learning Algorithm

“…Reservoir fluid recognition based on AdaBoost is realized by digitizing the input image and feature extraction. The unsupervised learning algorithm (such as neural network) is mainly aimed at the detection system with less damage or insensitive parameters [17][18].…”