Automatic crack detection based on image analysis is a hot issue in bridge structure health inspection. Crack segmentation based on deep learning is a significant solution, which needs lots of database. The small-scale crack images of open datasets are not sufficient for detection of long and thin cracks of super large-scale image. The study proposes an automatic crack detection method for super large-scale images, based on feature pyramid network. Through four different feature maps in various sizes, the proposed network yields predictions, respectively, which means a highly precise crack segmentation. Experiments are carried on 120 steel box girder crack images in a resolution of 3 264 pixels×4 928 pixels. These images are used to train and test the network. The comparison study is conducted between the proposed method and the models trained with crack images resized into 1 600 pixels×2 400 pixels and 2 112 pixels×3 168 pixels with bilinear interpolation algorithm. The results show that our method can achieve the highest crack Intersection over Union (IoU) of 0.78, and the lowest Dice Loss of 0.12 in the comparison study. The predictions of crack images in testing indicate that resizing images sometimes result in the loss of crack information, and our method can maintain the detail of cracks and detect cracks of super large-scale images automatically and precisely.