Development and optimization of object detection technology in pavement engineering: A literature review

Abstract

Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure, it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method. Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology. Due to the different characteristics of pavement distress and the uncertainty of the external environment, this kind of object detection technology for distress classification and location still faces great challenges. This paper discusses the development of object detection technology and analyzes classical convolutional neural network (CNN) architecture. In addition to the one-stage and two-stage object detection frameworks, object detection without anchor frames is introduced, which is divided according to whether the anchor box is used or not. This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition. Lightweight network architecture is introduced for mobile and industrial deployment. Since stereo cameras and sensors are rapidly developed, a detailed summary of three-dimensional object detection algorithms is also provided. While reviewing the history of the development of object detection, the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.