Road Blockage and Illegal Parking Identification Dataset

The current traffic industry faces increasingly severe issues of road blockages and illegal parking, affecting the smoothness and safety of urban traffic. Existing traffic monitoring systems often rely on manual inspections, which are inefficient and prone to omissions. This dataset aims to provide high-quality object detection data for smart traffic systems to automatically identify road blockages and illegal parking situations, thereby enhancing real-time monitoring capabilities. During the construction of the dataset, high-resolution cameras were used to shoot around the clock at major urban traffic intersections, and machine learning algorithms were employed for automatic annotation, ensuring data diversity and accuracy. Strict quality control measures were implemented, including expert reviews and multiple rounds of annotation, to ensure high quality and consistency of the data. The data is stored in JPEG format and organized according to image IDs for easy subsequent data processing and analysis.