# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 1 Introduction  
Structural health monitoring (SHM) is an essential way to prevent civil structures from damage as well as to improve their structural health via regular maintenance. SHM can be categorized into two approaches: vision-based inspection and sensor-based monitoring. The latter uses sensors installed on structures to continuously monitor structural changes by measuring structural responses, including displacement, strain, and acceleration. Vision-based inspection finds apparent damage or phenomena caused by structural damage, which have been recently observed, using optical devices combined with various computer vision (CV) techniques. The recent advances in CV techniques have had a high impact on today's SHM of civil structures. These techniques are generally noncontact, remote, and quick, with minimal interference to structural operation; hence, such CV techniques are promising as a supplement to conventional labor-based inspection. The inspection of concrete infrastructure is important, as these structures experience degradation through aging, earthquakes, or overloading. The degradation becomes apparent in the form of damage, such as cracks, spalling, rebar exposure, rebar corrosion, segregation, and efflorescence. Various studies have reported the detection of such damage using CV techniques [1, 2, 3]. Over the last decade, machine learning algorithms, such as k-means and self-organizing maps (SOMs), support vector machines (SVMs), naive Bayes classifiers, and feed-forward neural networks (FNNs), have been applied to detect various types of structural damage [4, 5, 6]. However, following the evolution of convolutional neural networks (CNNs) and an increase in the amount of data, deep learning architectureshave been widely used for damage detection in concrete structures [7; 8; 9]. [PERSON] et al. [10] conducted a comparative study on crack detection to compare the CNN-based approach with the traditional Canny and Sobel edge detection methods. [PERSON] et al. [11] proposed a crack-detection model using fully convolutional layers with different dilation rates for feature-map extraction. [PERSON] and [PERSON] [12] conducted a study on crack morphology detection using a CNN-based architecture known as AlexNet. [PERSON] et al. [13] used three object detection models (AlexNet, VGGNet13 and ResNet18) for crack detection in concrete structures. Many studies have proposed crack detection and localization using deep learning models [14; 15; 16]. [PERSON] et al. [17] proposed a customized CNN-based model approach for crack detection and localization, which outperformed other pre-trained object detection models in terms of accuracy. To quantify the detected damage, damage must be both detected and pixel-wise segmented. Regarding pixel-wise segmentation, many researchers have used semantic and instance segmentation deep-learning models [18; 19; 20] for inspection. [PERSON] and [PERSON] [21] conducted a review of pixel-level crack segmentation and compared eight different deep learning models with a performance evaluation. [PERSON] et al. [22] used an artificial neural network for crack segmentation. They also proposed algorithms to analyze the length and width of cracks, known as thinning and tracking algorithms. [PERSON] and [PERSON] [23] proposed an encoder-decoder network called CrackNet for crack segmentation on a concrete roadway. In addition to concrete crack segmentation, [PERSON] et al. [24] proposed a deep learning architecture for crack segmentation on ceramic tiles. The authors focused on cracks irrespective of the background, and detected and separated cracks close to the grout. Damage, such as spalling, rebar exposure, and efflorescence, can occur in any concrete structure, along with cracks, and they are significant indicators of structural integrity. Efflorescence is a white salt residue that occurs mainly in areas with low temperatures and moist conditions.