Camera Lens Contamination Detection with Grid-Based Model Updates

The contamination detection model is trained and updated automatically using images captured by the camera system itself. The system collects real images, generates synthetic contaminated images through image processing, trains the model with these datasets, and updates the model weights without requiring external intervention or separate training hardware, thereby improving detection accuracy while minimizing system complexity

The system performs preliminary actions by pre-processing images to generate synthetic contamination samples before actual contamination detection is needed. By creating training datasets in advance through image processing techniques and pre-training the model with these prepared datasets, the system achieves high detection accuracy when actual contamination occurs without adding real-time processing complexity

The model updates are performed periodically rather than continuously in real-time. The system collects reference images over time, processes them batch-wise to generate training datasets, and updates the model weights at scheduled intervals. This periodic update approach maintains high detection reliability through continuous learning while avoiding the time penalty of real-time processing for each update

The system performs preliminary processing of reference images to create training datasets before model updates are needed. By pre-processing images, generating synthetic contaminated versions, and preparing training data in advance, the system can quickly update model weights when updates are required, thereby improving reliability without significant time loss during actual detection operations