# An Improved Crack Identification Method for Asphalt Concrete Pavement
###### Abstract  
The results of high-precision asphalt concrete pavement crack identification can provide help for pavement maintenance. Therefore, methods of image feature enhancement and crack identification of asphalt concrete pavement cracks are proposed. First of all, we used an industrial CCD camera mounted on a vehicle to collect an asphalt concrete pavement crack image. Then, after using the NeighShrink algorithm to denoise the acquired image, a fractional differential image enhancement algorithm was designed based on image feature blocks to enhance the image features. On this basis, crack characteristics were segmented and processed by watershed algorithm. Through crack direction identification and crack parameter extraction, crack distribution direction, crack length and width and other parameters of asphalt concrete pavement were obtained in order to achieve accurate identification of asphalt concrete pavement cracks. The experiment found that this method can effectively remove noise information from asphalt concrete crack images; after applying this method, the image entropy value of each image was improved, with a minimum improvement of 0.38 and a maximum improvement of 1.98. The time consumed by this method in identifying cracks in asphalt concrete pavement varied between 1.4 s and 2.4 s. When identifying the length of cracks in asphalt concrete pavement, the maximum deviation value was only 0.47 mm; when identifying the width of cracks in asphalt concrete pavement, the maximum deviation value was only 0.31 mm. The above results indicate that by enhancing the image features of asphalt concrete cracks, this method achieves more accurate identification results for crack distribution direction, length and width values, with high identification efficiency and good application effect. s +
Footnote †: journal: Applied sciences
road conditions can be accurately and quickly mastered, and the automatic detection of pavement models can be realized. At present, the most traditional road detection work is usually completed by manual field walk-through. This detection method is generally carried out by professionals to detect pavement defects, analyze the severity of the road and carry out evaluation [3]. Due to the random distribution and wide space characteristics of pavement cracks, this detection method is very labor-intensive and requires many material resources, and the dangerous sections need to be tightly sealed when collecting and detecting in the field, which causes a certain degree of traffic safety hazards; therefore, it is difficult to achieve automatic pavement crack detection using the above traditional artificial pavement crack detection technology [4]. Now, there are also many domestic and foreign scholars studying pavement crack identification methods, such as [PERSON] et al., who put forward the method of terrain-guided UAV to identify tension cracks in open pit mines [5]. The method uses a UAV to collect ground crack images; the UAV flies at a constant altitude according to a flight path preprogrammed to generate mosaic and depth-map images. Next, the workbench, capture platform and channel are automatically identified and represented by their axes. Then, waypoints from each axis are sequentially uploaded to the UAV to scan the corresponding area with high resolution. Then, these high-resolution images are used to depict tension cracks and realize crack identification. However, the cost of this method is too high, and its application scope is small. [PERSON] et al. proposed a multimodal robot vision crack detection method, which uses videos/photos taken by a vehicle-borne robot camera to locate cracks in a remote environment [6]. The region of interest is identified and then explored by robots with tactile sensors. Faster R-CNN object detection is used to identify the location of potential cracks. Random forest classifier is used for tactile identification of cracks to confirm the existence of cracks, but the method is not accurate enough to identify cracks. [PERSON] et al.