Parking Space Identification Using Semantic Grid Segmentation

The patent replaces ultrasonic radar sensors with a camera-based visual detection system. The camera captures images of the surrounding environment, and image processing algorithms identify parking spaces by analyzing visual features such as lines, regions, and semantic information. This substitution of mechanical sensing with optical sensing and computational analysis significantly improves identification efficiency while reducing time consumption.

The patent creates a digital representation of the physical parking environment by capturing images with a camera and processing them through image analysis algorithms. Instead of directly measuring physical distances with ultrasonic sensors, the system creates an image-based model of the scene, segments it into grids, and identifies parking spaces through semantic analysis of the copied visual information, thereby improving detection speed and efficiency.

The patent divides the captured image into multiple grid regions and further segments each grid into sub-regions for detailed analysis. By segmenting the image space and processing each segment independently with appropriate algorithms, the system achieves higher identification accuracy for parking spaces while maintaining efficient processing through parallelization of the segmented regions.

The patent applies different processing strategies to different regions of the image based on their characteristics. For example, certain grids may receive more detailed semantic segmentation while others use simpler line detection. This localized adaptation of processing quality optimizes both accuracy for critical regions and efficiency for less critical regions, resolving the contradiction between precision and productivity.