Vehicular Detection and Classification for Intelligent Transportation System: A Deep Learning Approach Using Faster R-CNN Model (original) (raw)
2019, International journal of simulation: systems, science & technology
Intelligent Transportation System (ITS) is one of the attributes that describe smart cities. One of its functions is detection and classification of vehicles that pass through roadways. With this information, traffic management sectors can plan and implement road rules for the betterment of the traffic flow. Vision-based approaches and other methods, however, work only in ideal environment which make researchers find new ways on how limitations like occlusions, nighttime and camera angle can be solved. This paper demonstrates using a deep learning method to accurately detect and classify vehicles on urban roadways in a certain city. Additionally, a vehicle classifier was built and tested using a machine learning framework known as TensorFlow. Faster R-CNN model, with captured CCTV-video as dataset, was used to train the vehicle classifier. The performance of the newlytrained classifier has been evaluated using different classification metrics. Results show that using the proposed method, 93% accuracy and 78% F1-score in detecting and classifying vehicles were achieved based on labeled data. However, researchers also took note of the detection errors that showed during testing. Configurations in some steps has been provided to minimize such misclassifications. It was also recommended that the method be integrated as vital part of Intelligent Transportation Systems (ITS) in terms of vehicle detection and classification for future smart cities.
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