Vehicle Damage Recognition Using Part Database

The patent applies preliminary action by pre-collecting and organizing automotive part data from multiple vehicle models into a structured database before the actual recognition task. This pre-processing of data (performed in advance) allows the recognition algorithm to quickly access relevant part information without requiring extensive real-time training, thereby reducing the training period while maintaining recognition precision.

The patent changes the parameter approach by shifting from training the algorithm on extensive image data to querying a pre-organized database of automotive part parameters and specifications. Instead of improving precision through more training data, the system uses accurate parameter matching between detected parts and database records, reducing both training time and data requirements while maintaining high recognition precision.

The patent uses copying by creating a comprehensive database that stores standardized automotive part information from multiple vehicle models. Instead of collecting and processing extensive image data for each model, the system copies and stores essential part specifications, dimensions, and identification features in a structured format that can be efficiently queried during recognition, reducing implementation costs while maintaining accuracy.

The patent applies universality by designing a multi-functional database that serves multiple purposes: storing part specifications, enabling recognition matching, providing reference for damage assessment, and supporting various vehicle models simultaneously. This single universal data structure replaces the need for separate training datasets for each vehicle model, reducing overall implementation costs while maintaining recognition accuracy across diverse models.