Automatic Tire Inspection via Image Segmentation and Text Recognition

The patent replaces manual mechanical inspection with an automated optical inspection system using image acquisition devices, deep learning models, and computer processing. The system captures images of tires, processes them through segmentation and text recognition modules, and automatically determines tire conditions, eliminating human manual inspection while improving both speed and accuracy.

The patent creates a multi-functional inspection system that goes beyond单一 tire pressure monitoring. The system performs multiple functions including tire pressure assessment, tread depth measurement, tire condition evaluation, and text recognition (DOT codes, manufacturing dates). By integrating these diverse inspection capabilities into one system, it provides comprehensive tire information while maintaining high automation and speed.

The patent implements continuous automated inspection processing where image acquisition, segmentation, text recognition, and analysis occur in an uninterrupted automated sequence. The system continuously captures tire images, processes them through deep learning models in real-time, and generates comprehensive inspection reports without human intervention, thereby obtaining complete tire information much faster than manual methods while maintaining continuous operational flow.