Freight car target detection in a complex background based on convolutional neural networks

Freight car target detection plays an important role in railway traffic safety, which typically depends on artificial observation or conventional machine learning, with insufficient accuracy and high demand for an observer's physical strength and image quality. Motivated by the recent advances of the convolutional neural network in object detection, this study investigates how deep neural networks can be applied in freight car target detection to better solve the aforementioned problems. We propose a novel two-training method for freight car target detection; the method includes general training and special training. In addition, online hard example mining and deformable convolutional network are introduced to select hard examples and extract better features for the special training stage to improve the problem of tiny target detection in poor images obtained from freight car target detection. The proposed methods are verified using experimental results based on three aspects, i.e. indexes, visualization, and speed. High accuracy can be achieved with good recall and acceptable speed for freight car target detection applications. Finally, we illustrate the utility of using such a model to test high robustness for changes in image quality and other target detection tasks with slight modification.