This paper outlines the development of a nonintrusive alternative to current intelligent transportation systems using road-side video cameras. The use of video to determine the axle count and speed of vehicles traveling on major roads was investigated. Two instances of a convolutional neural network, YOLOv3, were trained to perform object detection for the purposes of axle detection and speed measurement, achieving accuracies of 95% and 98% mAP respectively. Outputs from the axle detection were processed to produce axle counts for each vehicle with 93% accuracy across all vehicles where all axles are visible. A simple Kalman filter was used to track the vehicles across the video frame, which worked well but struggled with longer periods of occlusion. The camera was calibrated for speed measurement using road markings in place of a reference object. The calibration method proved to be accurate, however, a constant error was introduced if the road markings were not consistent with the government specifications. The average vehicle speeds calculated were within the expected range. Both models achieved real-time speed performance.
Miles, V., Gurr, F., & Giani, S. (2022). Camera-Based System for the Automatic Detection of Vehicle Axle Count and Speed Using Convolutional Neural Networks. International Journal of Intelligent Transportation Systems Research, 20(3), 778-792. https://doi.org/10.1007/s13177-022-00325-1
Journal Article Type
Sep 9, 2022
Online Publication Date
Sep 17, 2022
Sep 2, 2022
Publicly Available Date
Nov 29, 2022
International Journal of Intelligent Transportation Systems Research
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