Highway Vehicle Type Recognition and Counting Dataset

In modern traffic management, there is an urgent need to enhance the ability to monitor and analyze road traffic flow. However, traditional sensor and manual monitoring methods are often costly and inefficient. Although current AI technologies have been applied, precise recognition and counting in complex highway environments still face challenges, such as low recognition accuracy and insufficient model generalization capability. This dataset aims to improve the accurate recognition and counting of vehicle types in highway traffic scenes through deep learning model training with video data. The data is collected using high-quality camera equipment in real highway environments, covering various climates, times of day, and traffic densities. Data quality is ensured through multiple rounds of annotation, consistency checks, and expert reviews, with an annotation team of more than 20 people from a background in traffic engineering and computer vision. Data preprocessing includes image frame extraction, denoising, normalization, etc., and all data is stored in an orderly structured MP4 file format.