Illegal Parking Recognition Using Multi-View Deep Learning

The patent replaces manual monitoring with an automated image recognition system that uses deep learning models (ResNet-50 for feature extraction, FC layer for classification) to automatically detect illegal parking. The system processes video images, extracts features, and classifies parking status without human intervention, thereby eliminating time consumption while maintaining implementation simplicity through standardized processing pipelines.

The system performs self-service by automatically detecting, recognizing, and reporting illegal parking incidents. The deep learning model processes images independently, making decisions about parking legality without requiring manual analysis. The automated workflow includes image acquisition, feature extraction, classification, and result output, all performed by the system itself rather than human operators.

The patent replaces manual monitoring with an automated deep learning-based recognition system. The ResNet-50 backbone extracts features from parking images, and the FC layer classifies them as legal or illegal parking. This substitution enables high productivity through automated processing of multiple images simultaneously, while the system complexity remains manageable due to the use of pre-trained models and standardized architectural patterns.

The system segments the monitoring task into distinct functional modules: image acquisition module, feature extraction module (ResNet-50), classification module (FC layer), and result output module. This segmentation allows each component to be optimized independently and facilitates easier implementation and maintenance, achieving high productivity without excessive overall complexity.

The patent employs deep learning models (ResNet-50 for feature extraction, FC layer for classification) to achieve high measurement precision in parking detection. The model processes video images, extracts meaningful features, and accurately classifies parking status. This automated approach maintains high productivity while improving detection accuracy compared to manual methods, as the deep learning model can identify subtle patterns and contextual information that humans might miss.

The system optimizes detection accuracy by adjusting model parameters and processing settings. The ResNet-50 architecture with its specific number of layers and filter sizes can be tuned to balance computational resources with detection precision. The FC layer classification thresholds and the image processing parameters (such as resolution and preprocessing techniques) are optimized to achieve the desired measurement precision for illegal parking detection.