Rail transportation

Engineering

Transport engineering

Discriminative model

Layer (electronics)

Joint (building)

Set (abstract data type)

Computer science

Catenary

Organic chemistry

Pattern recognition (psychology)

Image (mathematics)

Hough transform

Artificial intelligence

Structural engineering

Chemistry

Programming language

Deep learning

Artificial neural network

Convolutional neural network

Computer vision

Electrical engineering

IEEE Transactions on Instrumentation and Measurement

Split pins (SPs) play an important role in fixing joint components on catenary support devices (CSDs) of high-speed railway. The occurrence of loose and missing defects of SPs could make the structure of CSDs unstable. In this paper, we present a three-stage automatic defect inspection system for SPs mainly based on an improved deep convolutional neural network (CNN), which is called PVANET++. First, SPs are localized by PVANET++ and the Hough transform & Chan-Vese model, and then, three proposed criteria are applied to detect defects of SPs. In PVANET++, a new anchor mechanism is applied to produce suitable candidate boxes for objects, and multiple hidden layer features are combined to construct discriminative hyperfeatures. The performance of PVANET++ and several recent state-of-the-art deep CNNs is compared in a data set that is collected from a 60-km rail line. The results show that our model is superior to others in accuracy, and has a considerable speed.

A CNN-Based Defect Inspection Method for Catenary Split Pins in High-Speed Railway