# Implementation of Pavement Defect Detection System on Edge Computing Platform
## 2 Related Works  
The ability of object image recognition is the key technology at the core of this research. In this study, we installed two lenses with different FoVs on a vehicle to capture images of the road pavement ahead. We also used YOLOv3, a DL object recognition model, to detect road pavement defects in front of the vehicle. Road defects identified in this paper are mainly classified into six categories: road potholes, transverse cracks, longitudinal cracks, transverse patches, longitudinal patches, and manhole covers, where the pothole category is primarily defined as the pothole in which the pavement defects must surpass 10 cm and the vehicle's wheels can fall into. Most related literature or commercial implementations employed high-resolution lidar, high-intensity infrared sensors, and other high-cost perception equipment for pothole detection [9; 10; 11; 12; 13; 14]. Their main purpose is to provide road repair engineering units for subsequent repair by sensing whether the current road is defective. [PERSON] et al. [15] used a high-intensity near-infrared light-emitting diode mounted under the front bumper of a vehicle to measure whether the current road surface is dry, slippery, or icy to give the driver a hint about the current road conditions. However, only relatively close potholes could be identified, and this is prone to misinterpretation. Ground-penetrating radar (GPR) has been frequently used to detect potholes and cracks to understand a continuous profile of existing road conditions. However, GPR is very expensive and requires well-trained, experienced operators; moreover, the result shows the possibility of crack detection that depends on the layers of cracks [16]. In addition, various academic institutes and scholars have used picture recognition to identify road conditions as a consequence of the advancement in image processing and sensor technologies. However, the literature on the identification of road pavement defects is inadequate, especially in the first-person perspective for pavement defect detection. [PERSON] et. al. [17] used a nonlinear support vector machine model with a Gaussian radial basis function to detect potholes. However, this scheme is limited by the ambient light source and weather factors, and it needs quite a bit closer recognizable distance. [PERSON] et al. [18] used binocular vision for image capture of road pavements and identified the road potholes based on a parallax stereogram. This strategy, though, requires fairly close proximity to identify details regarding potholes. Moreover, when the illuminance is low at night or in adverse weather, the algorithm becomes invalid. [PERSON] et al. [19] used a wide-angle lens mounted on the top of a vehicle to capture images of road pavement ahead. Simultaneously, a perspective projection transformation approach was used to detect road potholes in the aerial image using a blob recognition algorithm. This technique only provided improved detection performance for potholes at relatively short distances and is insufficient for all-weather applications. The watershed approach detection of road potholes was also used by [PERSON] et al. [20] in detecting road potholes with smooth, broken, and cracked pavements. However, the recognition rate is not high and is limited by the ambient light source. Recently, DL techniques have been achieving state-of-the-art results for object detection on standard benchmark datasets and computer vision competitions. In 2019, [PERSON] et al. [21] employed fully convolutional neural networks with semantic segmentation efficacy and virtual samples generated by autoencoders. They used a semisupervised learning approach to conduct road pavement hazard detection. Moreover, [PERSON] et al. [22] proposed a pothole classification model using edge detection and a YOLO-based feature extraction scheme for pothole detection. However, these studies have only considered ideal situations such as daytime on a sunny day.