  Railway transportation is the artery of China's national economy and plays an
important role in the development of today's society. Due to the late start of
China's railway security inspection technology, the current railway security
inspection tasks mainly rely on manual inspection, but the manual inspection
efficiency is low, and a lot of manpower and material resources are needed. In
this paper, we establish a steel rail fastener detection image dataset, which
contains 4,000 rail fastener pictures about 4 types. We use the regional
suggestion network to generate the region of interest, extracts the features
using the convolutional neural network, and fuses the classifier into the
detection network. With online hard sample mining to improve the accuracy of
the model, we optimize the Faster RCNN detection framework by reducing the
number of regions of interest. Finally, the model accuracy reaches 99% and the
speed reaches 35FPS in the deployment environment of TITAN X GPU.
