# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 2 Related Work  
The majority of previous studies have used computer vision to detect line markings that delineate traffic lanes. Together with other modules such as obstacle detection [11], such studies have aimed to support advanced driver assistance systems (ADAS) and the autonomous driving industry. On the basis of extracted lane line markings on road pavements, various models have been developed to fit the geometric shape of lanes, including, but not limited to, linear models, parabolic models, hyperbolic models, the Clothoid model and the spline model. The distinctive features for the detection of line markings vary. For example, [PERSON] et al. [12] utilized color information in the YCbCr color space, i.e., yellow and white, to identify line markings and achieved an average detection rate of 93%, regardless of various illumination conditions. While [PERSON] et al. [13] relied on vertical gradients of images to identify line markings and achieved a 95.54% detection rate. Specifically, the LSPectra variable, the characteristic spectrum of line markings, was first obtained and used to match line marking points. Alternatively, deep learning algorithms have been employed to replace feature extraction steps, as developed in [14], where [PERSON] et al. adopted a fully convolutional neural network (CNN) to achieve the pixel-wise detection of lane lines. The detection accuracy of the best model reached 82.24% on the test dataset, where there were high reflection cases. To further assist autonomous driving, [PERSON] et al. [15] proposed a general framework for an intelligent transportation system by employing Binarized Normed Gradient (BING) features and a principal component analysis (PCA) network to classify a wide range of road markings including arrows, speed signs, \"RAIL\" signs, etc. For all of the above-mentioned studies, although they achieved pavement marking detection with satisfactory accuracy/precision, they did not classify them to fulfill management requirements such as inventory data collection. Another group of research falls into the digital asset management study area. Such studies have aimed to obtain condition information of road assets, by using a combination of image processing techniques and machine learning algorithms. They managed to identify various defects on roadway pavements, classify them and evaluate their severity. Defects targeted in previous studies include cracking, rutting, potholes and patches. As regards the first task (i.e., defect detection), [PERSON] et al. [16] and [PERSON] et al. [17] achieved the automatic detection of cracking on asphalt pavement, using a combination of a multi-class Support Vector Machine (SVM) classifier and an artificial bee colony optimizer, and a naive Bayes-based machine learning algorithm, respectively. The detection accuracy reached more than 0.96 in [16]. A few studies targeted potholes for detection [18; 19], where [PERSON] [18] further localized them explicitly using graph-cut segmentation and achieved a precision of 90%. [PERSON] and [PERSON] [20] developed a real-time mobile-based system to recognize several types of damage, i.e., pothole, longitudinal-transverse cracking and fatigue cracking. The precision, recall, accuracy and F-measure were satisfactory (more than 0.7), as regards the mobile platforms the system operated on. To cope with a major limitation of the aforementioned studies--that only one particular type of distress can be dealt with at one time--[PERSON] et al. [21] proposed to classify longitudinal and transverse cracks using Gabor filters and an AdaBoost algorithm. [PERSON] et al. [22] developed an approach to further categorize pavement cracks into superficial, crocodile, linear and transverse. In recent years, deep learning approaches have been increasingly deployed in a wide range of industrial applications [23; 24; 25; 26].