# An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges
###### Abstract  
This paper mainly improves the visual geometry group network-16 (VGG-16), which is a classic convolutional neural network (CNN), to classify the surface defects on cement concrete bridges in an accurate manner. Specifically, the number of fully connected layers was reduced by one, and the Softmax classifier was replaced with a Softmax classification layer with seven defect tags. The weight parameters of convolutional and pooling layers were shared in the pre-trained model, and the rectified linear unit (ReLU) function was taken as the activation function. The original images were collected by a road inspection vehicle driving across bridges on national and provincial highways in Jiangxi Province, China. The images on surface defects of cement concrete bridges were selected, and divided into a training set and a test set, and preprocessed through morphology-based weight adaptive denoising. To verify its performance, the improved VGG-16 was compared with traditional shallow neural networks (NNs) like the backpropagation neural network (BPNN), support vector machine (SVM), and deep CNNs like AlexNet, GoogLeNet, and ResNet on the same sample dataset of surface defects on cement concrete bridges. Judging by mean detection accuracy and top-5 accuracy, our model outperformed all the contrastive methods, and accurately differentiated between images with seven classes of defects such as normal, cracks, fracturing, plate fracturing, corner rupturing, edge/corner exfoliation, skeleton exposure, and repairs. The results indicate that our model can effectively extract the multi-layer features from surface defect images, which highlights the edges and textures. The research findings shed important new light on the detection of surface defects and classification of defect images. t 1 20202028 January  
[PERSON] et al. [5] introduced deep migration learning to detect surface cracks of roads made of hot mix asphalt (HMA) and Portland cement concrete (PCC), and observed the best detection effect on the single-layer neural network (NN) after feature training. Based on a convolutional neural network (CNN), [PERSON] et al. [6] conducted finite-element simulation of cracked gusset plate connections in steel bridges, and differentiated between defected and healthy bridge samples. [PERSON] et al. [7] proposed a CNN method based on the sliding window, which uses deep learning frameworks from AlexNet and GoogLeNet to classify bridge damages. [PERSON] and [PERSON] [8] trained the CNN with the track defects in the Photometric Stereo Images Database. The defects are cavities on the track surface, which indicate further surface degradation before track breakage. [PERSON] et al. [9] proposed an NN to classify images on bridge surfaces, and compares the expected reflection features of defected bridge surfaces and those of intact bridge surfaces. Specifically, the classical CNN is trained in a purely supervised manner, and the influence of different regularization methods is discussed including unsupervised layered pre-training and enhancement of bridge defect datasets. [PERSON] et al. [10] developed a CNN-based approach to detect fatigue cracks from real crack images at triangular plate joints, which are the most fatigue-prone joints of steel bridges. By this approach, the image datasets are collected from previous fatigue tests and bridge checks under uncontrolled settings. Next, the pre-trained benchmark CNN architecture on large general image datasets is adopted to transfer the deep learning features to bridge crack classification tasks. [PERSON] et al. [11] put forward a novel road crack detection algorithm based on deep learning and adaptive image segmentation. First, the deep CNN was trained to judge the presence of cracks in images. Then, the crack-containing images were smoothed by a bilateral filter to minimize the number of noise pixels. Lastly, the cracks were extracted from pavement through adaptive thresholding. [PERSON] et al. [12] designed a machine learning algorithm that adapts well to actual scenarios.