For the low precision of small target detection in the existing convolution neural network methods used in airport video surveillance, in this paper, a small target detection algorithm based on Faster-RCNN combined with multi-scale feature fusion and online-hard-example-mining(OHEM) is proposed. First of all, ResNet-101 is adopted as the feature extraction backbone, and a top-down multi-scale feature fusion pathway is established based on the ResNet-101 to generate richer semantic feature maps of a fine resolution. During the network training, OHEM is adopted to make the network more robust to locate the region of small target objects. At last, an airport dataset containing 5 982 pictures is constructed manually, which is used to verify the training and testing of the model. The results show that our modified Faster-RCNN algorithm significantly improves the accuracy of small target detection under airport situation. Besides, the mean average precision reaches 80.8%, which is higher than other advanced object detection models.