Abstract
Vehicle detection and classification have become important tasks for traffic monitoring, transportation management and pavement evaluation. Nowadays there are sensors to detect and classify the vehicles on road. However, on one hand, most sensors rely on direct contact measurement to detect the vehicles, which have to interrupt the traffic. On the other hand, complex road scenes produce much noise to consider when to process the signals. In this paper, a data-driven methodology for the detection and classification of vehicles using strain data is proposed. The sensors are well arranged under the bridge deck without traffic interruption. Next, a cascade pre-processing method is applied for vehicle detection to eliminate in-situ noise. Then, a neural network model is trained to identify the close-range following vehicles and separate them by Non-Maximum Suppression. Finally, a deep convolutional neural network is designed and trained to identify the vehicle types based on the axle group. The methodology was applied in a long-span bridge. Three strain sensors were installed beneath the bridge deck for a week. High robustness and accuracy were obtained by these algorithms. The methodology proposed in this paper is an adaptive and promising method for vehicle detection and classification under complex noise. It would serve as a supplement to current transportation systems and provide reliable data for management and decision-making.
Highlights
The detection and classification of vehicles are significant for transportation monitoring and bridge management
The machine learning algorithm is capable of identifying the vehicle following signals, which can be vital for vehicle detection in heavy traffic scenes
We propose a data-driven methodology, which is a solution for the detection and classification of vehicles using strain data under the bridge deck, containing sensors arrangement, cascade vehicle detection, vehicle following detection and vehicle classification
Summary
The detection and classification of vehicles are significant for transportation monitoring and bridge management. The most widely used sensors include acoustic sensors, inductive-loop sensors, magnetic sensors, strain sensors and image sensors These sensors have been studied and applied in traffic infrastructures to monitor passing vehicles. A strain sensor based methodology for vehicle detection and classification for an orthotropic steel girder (OSG) bridge is proposed. The close-range following vehicles (CRFVs) samples in the extracted samples are identified and processed by a trained artificial neural network (ANN) model and the non-maximum suppression (NMS) algorithm, respectively. The machine learning algorithm is capable of identifying the vehicle following signals, which can be vital for vehicle detection in heavy traffic scenes. The following NMS algorithm is effective for separation of CRFVs. Provision and evaluation of a residual block-based CNN model for vehicle classification.
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