In an era where food delivery is prevalent, situations such as deliverypersonnel delivering the wrong meals or restaurants preparing incorrectorders are inevitable. This study aims to develop a model to recognize theshapes of delivery meal containers. The research focuses on identifyingfive common types of containers, including bowls, bubble teacups, smallrectangular boxes, large rectangular boxes, and square bento boxes. Basedon the YOLOv8 model, this study designs an efficient recognition system
for meal containers, capable of accurately detecting the number and typesof containers in delivery boxes.By leveraging deep learning for objectdetection, the solution aims to reduce error rates in thefood deliveryprocess.
