## 1 Introduction
### DCNN Application in Segmentation  
Recently, the use of convolutional neural networks (CNN) for image classification tasks has been on the rise. This is due to its ability to extract complex features autonomously from large datasets [24; 36]. CNN has broad input, feature extraction, and classification layers. The feature extraction layers contain sub-layers such as convolutional, activation function, pooling, and batch normalization, while the classification layers have fully connected, drop-out, SoftMax, and output layers. The function of the feature extraction layer is primarily to determine a common pattern in the sets of images, such as edges, textures, shapes, and objects in the images. These features are, in turn, fed into the classification layer for the prediction or classification of the input image. Commonly adopted CNN models for image classification, AlexNet [36], VGG [37], GoogleNet, and ResNet [38; 39], are appropriate for image classification tasks.  
Deep learning has been broadly applied for defect detection, condition assessment, inspection, and evaluation of existing infrastructures. Studies have been conducted to investigate the performance of the DCNN for defect detection, such as delamination, cracks, spalling, and patches in visual and infrared thermography images of laboratory-modeled and in-service civil infrastructures. Past studies have conducted segmentation by deploying region-level or pixel-wise semantic segmentation procedures. Region-level segmentation in bridge inspections draws bounding boxes around damages. [PERSON] et al. [40] adopted Faster R-CNN, a region-based segmentation model, to detect structural defects. [PERSON] and [PERSON] et al., in their study, boost the detection performance of the model by introducing transient information using video sequences. This model is, however, limited by detecting various defects at the grid-cell level. [PERSON] et al. [41] investigated the MobileNetV2 model for multi-class damage detection in thermal images from an existing buttress dam. The classification model identified 79.7% of the defects with a reduction in false positives (FP) when the VGG 16 model was adopted. The encoder-decoder type of architecture has been used in past studies for pixel-wise classification in semantic segmentation tasks [42]. Fully convolutional networks (FCNs) have also been used with high prospects [43].  
DCNN models have been shown to perform optimally and better with more datasets. The effect of data augmentation was investigated by [PERSON] et al. [24]. Their studies revealed a drop in the model's performance after augmentation when considering the intersection over union (IoU). A general drop in IOU ranged from 12% to 19%. The authors stated that the model's overall performance for on-site use can be improved with more robust in-service data. The lack of a model trained with in-service data is a significant roadblock in deploying DCNN for the autonomous assessment of existing bridge decks.  
A review of the published literature indicates that autonomous delamination detection of in-service bridge decks is not common. Most models have been developed by training laboratory datasets subjected to active heating sources. These generated datasets do not depict the actual conditions of the bridge decks. The performance of models has been based on laboratory datasets. Therefore, these datasets are augmented to generate more datasets for training and validation. This study is carried out to develop a trained DCNN model with a publicly available IRT dataset for delamination detection and to evaluate the effect of augmentation, dataset balancing, and a combination of these on the performance of the developed models.