Dishwasher Camera Failure Detection Using Image Processing

The system performs preliminary detection of camera status by analyzing dish images before the washing cycle begins. The controller checks whether the captured image meets predetermined standards (clarity, brightness, color accuracy) and detects abnormalities such as panel contamination, lens damage, defective pixels, or color filter issues before they cause washing malfunctions. This preliminary action prevents the camera from failing during the actual washing process.

The system establishes a feedback mechanism where the controller continuously monitors the quality of images captured by the dish recognition camera. By analyzing image characteristics (brightness, color, clarity) and comparing them against predetermined standards, the system receives feedback on camera health status. When abnormalities are detected, the controller can alert users or automatically adjust operations, creating a closed-loop feedback system that maintains camera reliability.

The dishwasher performs self-diagnosis by using its existing image processing capabilities to detect camera failures. The controller analyzes dish images for abnormalities (panel contamination, lens damage, defective pixels, color filter issues) without requiring external detection devices. This self-service approach allows the system to monitor its own camera health using resources already available in the dishwasher.

The dish image itself serves as an intermediary that carries information about camera health status. By analyzing characteristics of the captured image (brightness distribution, color accuracy, presence of artifacts), the controller can indirectly detect camera failures without needing direct electrical or physical contact with the camera components. This intermediary approach enables failure detection through the camera's normal operating output.