# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 3 Literature Review  
### Vision-Based Object Recognition in Construction  
A construction project involves diverse materials, a range of equipment, and a variety of workers, creating a complex and dynamic environment to be predicted. Nowadays, digital cameras are widely used to record construction scenes. Besides increasing the frequency of collecting data, they have reduced the price for acquiring and maintaining the construction-site images [18]. By processing these images properly, the abundant project information can be effectively extracted, which provide an opportunity for automation in construction monitoring. To process visual data, the first step to be taken is to identify construction objects of interest. To achieve this goal, computer vision is widely used for various construction applications and also has a great potential to automate construction management processes [10; 19]. In particular, the characterization of construction resources (i.e., personnel, equipment, and materials) has been one of the major areas that image processing is successfully applied. Many studies have focused on detecting equipment, workers, vehicles, and specific types of construction damages such as cracks in concrete and steel surfaces [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. [PERSON] and [PERSON] [24] proposed a method to detect construction worker in video frames. The method exploited motion, shape, and color features to characterize workers. [PERSON] et al. [25] presented a method that detected construction workers and their actions using colors and depth information from a Microsoft Kinect sensor. They extracted meaningful visual features based on the estimated body pose of workers. [PERSON] et al. [26] used histograms of oriented gradients and colors for detecting construction workers and equipment from site video streams. [PERSON] and [PERSON] [27] demonstrated a rapid method to recognize and register dynamic heavy equipment operations in 3D views. Their method extracted and processed the target objects' point cloud data from the background scene and registered 3D target models from a model database to the corresponding point clouds. [PERSON] and [PERSON] [29] presented a hybrid method to locate construction workers. This method fused tracking and detection together throughout the tracking process. [PERSON] et al. [28] proposed a framework that integrated the visual tracking into the detection of the construction workforce and equipment. Like the above mentioned methods, in the structural health monitoring studies, deep convolutional neural networks are widely used for detecting civil infrastructure defects to partially replace human-conducted onsite inspections [30; 31; 32; 33]. Similarly, many researchers have conducted research on vision-based material recognition to aid automation in construction progress monitoring [19; 34; 35; 36; 37; 38; 39; 40]. [PERSON] et al. [35] proposed a method to extract the objects of interest from construction images using solid imaging algorithms and 3D computer-aided design. [PERSON] and [PERSON] [37] and [PERSON] et al. [41] presented geometry and appearance-based material classification methods for monitoring construction progress deviations at the operation level. The method leveraged 4D Building Information Models (BIM) and 3D point cloud models. [PERSON] et al. [19] demonstrated the data-driven scene parsing method to recognize various objects on a construction image. [PERSON] et al. [38] proposed a method to detected the components of an interior partition and infer its current state using 2D digital images. Some of the aforementioned methods were designed for real-time performance. For instance, the construction worker detection method proposed by [PERSON] and [PERSON] [24] can achieve near real-time (5 fps) performance. However, these methods generally could not provide enough information for construction site navigation. On the other hand, the other data-driven scene parsing methods like the one proposed by [PERSON] et al.