# Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion
## 1 Introduction  
Pavement detection and maintenance is supported by advanced technology, such as image sensing, big data and AI, to ensure the safety and transportation efficiency of the roads. Nowadays, with the rapid development of image acquisition techniques, a global shutter camera can realize fast imaging with a lower price than that of a ground-penetrating radar and laser system. However, the automatic processing algorithm of pavement images still remains challenging [1, 2, 3, 4]. Considering the difference in the pavement circumstance and the economic level of various areas, there is practical significance to study adaptable pavement detection methods [5]. So far, road pavement detection technology has been developed, from traditional image processing methods to deep learning techniques [6]. The traditional methods for image segmentation, including threshold image segmentation, an edge detection algorithm, texture segmentation and multi-feature fusion methods, are realized by an artificially designed feature [7, 8]. Therefore, they work out good results on specific datasets. Among them, [PERSON] et al. proposed a method for crack detection via a salient structure extraction from a textured background, which extracts strong edges and distinguishes them from strong textures in a local neighborhood [9]. [PERSON] et al. employed and computed famous scale-invariant feature transform (SIFT) features to establish the visual vocabulary of wordsto represent the pavement surface and employed a support vector machine (SVM) for the training and testing of potholes in pavement images [10]. [PERSON] et al. proposed a pavement crack detection method fused with a histogram of oriented gradients and a watershed algorithm of a range image [11]. [PERSON] et al. evaluated two machine learning algorithms for surface roughness based on statistical parameters [12]. In recent years, several road damage detection methods based on a convolution neural network (CNN) have been proposed and demonstrated to have good effects [13], including ASINVOS net [14], VGG [15], FS-Net [16] and YOLO [17]. For instance, [PERSON] et al. proposed a pavement crack detection method based on an end-to-end trainable deep CNN [18]. A deep learning algorithm significantly reduces the cost of the feature description, extraction and recognition, which further improves the accuracy and versatility of pavement detection methods [19, 20]. A deep learning neural network relies on its powerful fitting ability to realize pavement detection. However, training with high-resolution images, which contain much more information about the pavement, requires plenty of memory and computing power. The effective way is to locate the target of the high-resolution images before training the neural networks [21]. Nevertheless, the size of the damaged areas varies dramatically, which also have fuzzy boundaries. As a result, to pick up the damaged areas precisely from pavement images seems very difficult [22]. It is noticed that the grayscale of damaged areas is obviously lower than that of normal pavement, with apparent cracks and potholes. Dependent on this intuitive information, damaged areas could be separated roughly from the raw data and then be recognized by the existing detection methods. In this paper, we propose a road pavement damage detection deep learning model based on feature points from a local minimum of grayscale. First, the local minimum points of grayscale in an image window are selected to be the feature points according to the grayscale distribution. The image blocks, the neighborhood of these feature points, are then cut from the image window as individuals, because they contain the main information about the damage. The image blocks, with a small size, are collected as a dataset to train the CNN model, which could extract the deep feature of the damage. After that, the trained CNN model can be used to detect the practical pavement images.