| description abstract | The US Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve multiple transportation needs, such as road infrastructure design, pavement maintenance scheduling, and traffic-induced emission estimation. Although a plethora of studies have advanced computer vision-based techniques for vehicle classification, no vision-based method has yet achieved the desired level of accuracy for all 13 FHWA vehicle category classifications primarily due to the interclass similarity issue, particularly among trucks. To fill this gap, this study developed a two-stage vision-based method that leverages both semantic and geometric features extracted from surveillance videos. In the first stage, a cascaded Mask R-CNN model is employed to classify vehicles into six broad categories based on semantic features (i.e., vehicle appearance). In the second stage, the geometric features (i.e., axle configuration) are extracted and exploited to further classify trucks into nine specific FHWA categories. Additionally, a verification scheme is introduced to validate the classification results with the aim of filtering out the misclassified vehicles and improving the overall classification accuracy. The proposed method was evaluated through field experiments under different traffic scenarios. It achieved an overall classification accuracy of 98.6% across all 13 FHWA vehicle categories, with an additional 1.1% improvement thanks to the verification scheme. This study contributes to the body of knowledge by introducing a more accurate method for FHWA vehicle classification, particularly for trucks, expanding the potential of image-based techniques in a variety of intelligent transportation applications. | |
