IEEE Transactions on Intelligent Transportation Systems · 2015 · 66 citations · 37 references
Point CloudCartographyInterior SignageImage AnalysisMachine VisionEngineeringPattern RecognitionSpatial Data AcquisitionPoint Cloud ProcessingMobile Laser ScanningAutomatic IdentificationTraffic Sign PositionOptical Image RecognitionTraffic Sign DetectionAutomated InspectionLocation InformationComputer VisionSpatial Verification
The study introduces a spatial‑related traffic sign inspection process using mobile laser scanning data to support traffic sign inventory. The authors develop an MLS‑based pipeline that detects traffic signs via retroreflectivity, registers point clouds to 2‑D images, extracts sign positions and placements, recognizes sign types, and constructs a spatial‑associated sign network. Experimental results show satisfactory performance of the detection, recognition, position, and placement algorithms and demonstrate MLS data’s potential for automatic traffic sign inventory.
This paper presents a spatial-related traffic sign inspection process for sign type, position, and placement using mobile laser scanning (MLS) data acquired by a RIEGL VMX-450 system and presents its potential for traffic sign inventory applications. First, the paper describes an algorithm for traffic sign detection in complicated road scenes based on the retroreflectivity properties of traffic signs in MLS point clouds. Then, a point cloud-to-image registration process is proposed to project the traffic sign point clouds onto a 2-D image plane. Third, based on the extracted traffic sign points, we propose a traffic sign position and placement inspection process by creating geospatial relations between the traffic signs and road environment. For further inventory applications, we acquire several spatial-related inventory measurements. Finally, a traffic sign recognition process is conducted to assign sign type. With the acquired sign type, position, and placement data, a spatial-associated sign network is built. Experimental results indicate satisfactory performance of the proposed detection, recognition, position, and placement inspection algorithms. The experimental results also prove the potential of MLS data for automatic traffic sign inventory applications.
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