Real-time image annotation is transforming modern parking management systems. These systems rely on high-quality, continuously annotated datasets to train and improve their algorithms. However, the implementation of image annotation for parking is not without its hurdles. Quality issues can arise both in the raw data being captured and in the final annotated datasets used for training. These data quality issues can compromise the effectiveness of the entire parking management system.
In the following sections, we’ll explore the specific challenges faced in maintaining high-quality data during both the capture and real-time annotation phase, and discuss strategies to overcome them, ensuring more robust and effective parking applications.
