Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

Abstract

Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.

Accessible Routes Integrating Data from Multiple Sources

“…It is an effective method for improving the sidewalk network, as well as detecting pedestrian crossings and obstacles on the sidewalk. In addition, point clouds allow the recognition of other elements of the road network [36,37]. However, the cost of the equipment and the effort to apply it to a whole city is still high, and hence it could be used initially in selected areas with greater interest (e.g., because of the higher flow of people).…”

A Review of Mobile Mapping Systems: From Sensors to Applications

“…Recent trends of MMSs aim to perform direct georeferencing that leverages the capabilities of a multi-sensor platform [2,3], to minimize the human interventions during the data collection and processing. The automation has been further strengthened to use machine learning/artificial intelligence to perform online/offline object extraction and mappings, such as traffic lights and road sign extraction [4][5][6].…”

Nighttime Mobile Laser Scanning and 3D Luminance Measurement: Verifying the Outcome of Roadside Tree Pruning with Mobile Measurement of the Road Environment

“…Mobile mapping systems are widely used for the 3D measurement of roads and built environments [20][21][22][23][24], forests and vegetation [25], and especially for collecting data for urban tree inventories [26,27]. Furthermore, luminance measurements have been integrated into point clouds scanned terrestrially and from mobile platforms [28][29][30].…”