Automatic Extraction of Roadside Traffic Facilities From Mobile Laser Scanning Point Clouds Based on Deep Belief Network

Abstract

Traffic facilities extraction is of vital importance to various applications such as intelligent transportation systems, infrastructures inventory and city management related applications. Mobile laser scanning (MLS) systems provide a new technique to capture and update traffic facilities information. However, classifying raw MLS point clouds into semantic objects is still one of the most challenging and important issues. In this study, we separate the raw off-ground point clouds into individual segments and explore an object-based Deep Belief Network (DBN) architecture to detect roadside traffic facilities (trees, cars, and traffic poles) with limited labeled samples. To deal with various roadside traffic objects with different types, sizes, orientations and levels of incompleteness, we develop a simple and general multi-view feature descriptor to characterize the global feature of individual objects and extend the quantity of the training samples. Extensive experiments are employed to evaluate the validities of the proposed algorithm with six test datasets acquired by different MLS Systems. Four accuracy evaluation metrics precision, recall, quality and Fscore of trees, cars and traffic poles on the selected MLS datasets achieve (96.08%, 97.61%, 93.86%, 96.81%), (97.55%, 94.10%, 91.69%, 95.58%) and (94.39%, 97.71%, 92.37%, 95.99%), respectively. Accuracy evaluations and comparative studies prove that the proposed method has the ability of achieving the promising performance of roadside traffic facilities detection in complex urban scenes.