Automatic digitalization of railway interlocking systems engineering drawings based on hybrid machine learning methods

Engineering drawings of the railway interlocking systems come often from a legacy since the railway networks were built several years ago. Most of these drawings remained archived on handwritten sheets and need to be digitalized to continue updating and safety checks. This digitalization task is challenging as it requires major manual labor, and standard machine learning methods may not perform satisfactorily because drawings can be noisy and have poor sharpness. Considering these challenges, this paper proposes to solve this problem with a hybrid method that combines machine learning models, clustering techniques, computer vision, and ruled-based methods. A fine-tuned deep learning model is applied to identify symbols, labels, specifiers, and electrical connections. The lines representing electrical connections are determined using a combination of probabilistic Hough transform and clustering techniques. The identified letters are joined to create the labels by applying rule-based methods, and electrical connections are attached to symbols in a graph structure. A readable output is created for a drawing interface using the edges from the graph structure and the position of the detected objects. The method proposed in this paper can support the digitization of other engineering drawings assisting in solving the challenge of digitizing engineering schemes.