FHWA Vehicle Classification Dataset

This study introduces a significant advancement in vehicle classification, addressing the challenge of limited annotated datasets compliant with Federal Highway Administration (FHWA) guidelines. We present a novel benchmark dataset meticulously curated from various sources to capture variations in time, resolution, camera position, and weather conditions. With a total of 17,174 annotated instances across 7,980 frames, this dataset offers a remarkable granularity for vehicle classification, making this study the first of its kind, to the best of the author’s knowledge. Our research also aims at detecting vehicle subcategories within the FHWA's classification scheme. Acknowledging the visual complexity in distinguishing vehicles with similar appearances but differing weights according to FHWA's criteria, we propose a refined classification system. This system categorizes vehicles into six subcategories based on axle count and spacing, facilitating easier and more precise classification. In addition, we proposed an improved YOLOv5 model that incorporates the Convolutional Block Attention Module (CBAM). The proposed model achieved performance scores of 0.981, 0.965, and 0.985 for precision, recall, and mAP_@50, respectively. As a result, the proposed model outperformed all previous YOLO iterations on the experimental test dataset.  The addition of CBAM improves feature representation by focusing on important elements while ignoring irrelevant ones. The results show that the YOLOv5-CBAM integration is more precise and faster.