AI Based Detection, Material Classification and Dishwasher safety predictions for kitchen vessels

Abstract

Dishwashers are widely used in modern homes. However, improper placement of kitchen items like aluminum utensils, pressure cookers, and plates made of stainless steel, ceramic coated, and other materials can cause damage, safety issues, and reduce the lifespan of the appliance. To address this, this paper introduces an AI-based system that automatically detects, classifies materials, and predicts the safety of kitchen items in dishwashers. The system uses a dual network approach. One part uses YOLO for object detection to identify kitchen items and separate their components. The other part uses ResNet-50, a type of CNN, to classify materials into categories such as aluminum, stainless steel, iron, copper, ceramic, and mild steel. Based on the structure and material classification, the system predicts whether an item is safe or not for the dishwasher. Experimental results show that the system accurately detects items and reliably classifies materials, leading to better safety predictions. It also provides real-time visual feedback with bounding boxes, labels, and safety information. This system can be integrated into smart dishwashers and intelligent kitchen automation systems.