# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 1 Introduction
Spalling or potholes occur when water in a concrete structure undergoes multiple freeze-thaw cycles or when the rebar inside the concrete swells due to corrosion. Rebar exposure mainly occurs in concrete structures owing to the pouring of concrete mixtures with water deficiency or spalling. It is usually accompanied by spalling or cracks and is always detected with other concrete damage [25]. [PERSON] et al. [26] modified a fully convolutional network to detect multiple forms of damage via semantic segmentation in concrete structures, including cracks, spalling, rebar exposure, and holes. [PERSON] et al. [27] detected corrosion and cracks by improving segmentation accuracy using two methods of data input: (i) squashing segmentation, to input images with high resolution and (ii) cropping segmentation, to crop the image into a desired resolution. Although deep learning model performances are material-specific, their performances were found to be inadequate when a pre-trained model was tested on different material images with the same task [28]. Therefore, [PERSON] et al. [29] proposed a multi-task semantic segmentation model called MaDnet to detect multiple forms of damage in concrete, steel, and asphalt. For real-time damage detection, many studies have been conducted using fast R-CNN [30] and mask R-CNN [31]. [PERSON] et al. [32] used the fast R-CNN model to detect multiple forms of damage in real-time masonry structures. [PERSON] and [PERSON] [33] proposed a study based on mask R-CNN for instance segmentation, and they detected multiple forms of damage in a concrete structure. The abovementioned studies sought to achieve better damage detectability by implementing highly complex models with numerous trainable parameters. Thus, the models in these studies performed well on high-performance computers with several GPUs. However, in the past decade, various small edge computing devices (ECDs) have become available, including smartphones, Jetson, Coral, Raspberry Pi, and Arduino [34; 35; 36]. These ECDs connect to, or can be easily integrated with, imaging sensors, and they can be used by practitioners to build portable inspection devices that can detect and quantify damage to the field. In particular, smartphones have improved dramatically in terms of processors, memory, batteries, and communication; thus, smartphones have recently been used in various SHM applications [37; 38; 39]. To use ECDs as the operating platforms of deep learning models, lightweight models with a relatively small number of tunable parameters and appropriate performance are preferred. [PERSON] et al. [40] proposed a lightweight network known as MobileNet for object classification. The authors introduced depth-wise separable convolution instead of standard convolution, resulting in a lightweight network, by decreasing the parameters from 29.3 million to 4.2 million. [PERSON] et al. [41] proposed the YOLObile framework, which is a real-time object detection model for mobile devices. They implemented their model on a mobile device (Samsung Galaxy S20) and achieved an inference speed of approximately 17 FPS. In addition to classification and detection models, some research studies have been conducted on lightweight semantic segmentation models. [PERSON] et al. [42] proposed the first lightweight semantic segmentation model, known as ENet, which constitutes only 0.4 million parameters. Subsequently, other state-of-the-art models have been proposed that are supposed to be efficient for mobile devices [43; 44; 45]. However, the application of these models to damage segmentation tasks has rarely been reported in the literature. In summary, the main contributions of the proposed study are as follows:  
1.