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Overhead contact systems are important power supply equipments for electrified railway locomotives. If there are faults in the equipment, it will threaten the safety of train operation. Dropper faults and foreign matter are two common types of Overhead contact systems faults, the fault regions of which occupy a relatively small area in the whole image, so it is difficult to detect the faults accurately by capturing detailed features using original Faster R-CNN. To solve the above problem, an improved Faster R-CNN based on convolutional neural networks, named multi-view Faster R-CNN, is proposed in order to extract more details of the fault regions by combining deep and shallow feature maps. Experiments on the images collected from the Lanzhou-Xinjiang Railway line show that the feature fusion can significantly enhance the mean average precision. The precision of our model on the test set was 89.53%, increasing by 3.5%, 9.65%, and 5.8% compared with YOLOv3, SSD, and Faster R-CNN, and the detection accuracies of our model for unforced droppers and foreign matter were 88.17% and 90.60%, respectively, under a recall rate of almost 1.0. Because the multi-view feature fusion model in our method can flexibly detect faults with various sizes, it has an important application value in the fault detection of an overhead contact system. [ABSTRACT FROM AUTHOR]