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Pixel-wise Crack Identification of Steel Box Girder by Fully Convolutional Network

Steel box girder is the main component of modern long-span cable system bridges. And crack is the primary sign of problems with girders, thus its identification is very important. Lots of works have been done to solve this issue by cutting raw images into small blocks, then segmentation of cracks is treated as classification of blocks. How- ever, this type of method cannot be accurately segment cracks at the pixel level. Based on this deficiency, this paper proposes a new approach for pixel-wise crack identification of steel box girder by fully convolutional network (FCN). FCN is a new kind of network for pixel-wise segmentation, consisting of only convolutional layers, without fully connected layers. This study presents a new FCN, built based on modified U-Net to adapt to crack identification problem. The number of convolutional layers is adjusted to extract more scaled features. Different feature fusion method is proposed to take full advantages of multi-scale convolutional features. Then, a dataset called APESS2018 Steel Girder Crack ID Dataset is employed for training and validating the network. The dataset includes 50 raw images and corresponding pixel-wise label images with a size of 4928 × 3264 pixels, including four classes: cracks, ruler, handwriting and girder surface (i.e., background). Due to the limitation of computation power, the input size of the network is changed as 544 × 544 pixels. Thus, the Crack ID Dataset is necessary to be cropped and regrouped to 2700 sub-images. 94% of these sub-images are train-validating set with 10% validating rate, 6% are testing set. 180 epochs are trained with multi-step learning rate from 0.01 to 0.0001. Finally, the accuracy of proposed method is validated, 98.906% for training accuracy and 97.525% for testing. Different loss functions (Categorical Cross-Entropy loss and Dice Coefficient loss) are discussed to illustrate their influence on results.