Abstract
Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.
Highlights
Pavement condition measurements are essential for maintenance decisions [1]
Since the background of the pavement is monotonous and the algorithm can falsely match the features of the asphalt pavement, we propose the use of the spectral saliency mapping (SSM) method along with a pavement distress bounding box to extract information from dense regions. e scale-invariant features extracted from the key region serves as the stitching points between two images
Tx − tk where v is the true value of the velocity, l is the distance between two locations, GPSX is the global position system (GPS) location of Px and tx is the timestamp of Px, and GPSK is the GPS location of Pk and tk is the timestamp of Pk
Summary
Pavement condition measurements are essential for maintenance decisions [1]. Pavement distress detection has traditionally been a highly laborious and time-consuming task [2]. To solve the problems mentioned above, we propose a pavement distress stitching method to preprocess detected data. Is paper applies the image-stitching method in pavement detection to solve engineering application problems. Based on the above problems, we present a pavement distress image stitching method based on a feature-matching algorithm. Full videos were stored in a vehicle-mounted terminal while clipped images were uploaded at a frequency of 2 Hz. Several typical pavement distress defects on the urban road in Shanghai are considered in this paper, including cracks, patched cracks, potholes, patched potholes, nets, patched nets, and manhole covers (Figure 2). Tx − tk where v is the true value of the velocity, l is the distance between two locations, GPSX is the GPS location of Px and tx is the timestamp of Px, and GPSK is the GPS location of Pk and tk is the timestamp of Pk
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
