Development of a new speed measurement technique based on deep learning

The more pervasive use of Artificial Intelligence (AI) has enabled feature extraction enhancement in several fields, particularly in image processing applications. Thanks to AI, it is possible to use low-cost devices (e.g., webcams and surveillance cameras in complex scenarios) like vehicle speed measurement, obtaining a significant reduction of instrument costs and a great spread of their use. The paper presents a new industrial measurement instrument for vehicle speed based on non-dedicated hardware devices and innovative image processing methods like Regional CNN (Convolutional Neural Network). The proposed hardware is based on a generic surveillance camera or even a simple webcam and an R-CNN to transform it into an intelligent tool capable of estimating the speed of a vehicle and tracking its movement under controlled conditions. One of the essential aspects of the work concerns the metrological characterization of the proposed method. Measurement uncertainty has been evaluated. The metrological characterization of approaches using artificial intelligence can be fundamental for spreading such technologies in practical scenarios and impulse the industrial development of enhanced tools that can comply with legal regulations for speed measurement. The measured velocities of a car under test have been compared with a reference constituted by the vehicle speed retrieved by the ABS ECU.