Cooking State Recognition from Images Using Inception Architecture
This addresses the under-researched issue of object state detection for kitchen robots, but it is incremental as it builds on existing Inception models.
The paper tackled the problem of recognizing cooking states from images for kitchen robots by proposing a modified Inception architecture, achieving results that demonstrate its potential as a solution on a cooking dataset.
A kitchen robot properly needs to understand the cooking environment to continue any cooking activities. But object's state detection has not been researched well so far as like object detection. In this paper, we propose a deep learning approach to identify different cooking states from images for a kitchen robot. In our research, we investigate particularly the performance of Inception architecture and propose a modified architecture based on Inception model to classify different cooking states. The model is analyzed robustly in terms of different layers, and optimizers. Experimental results on a cooking datasets demonstrate that proposed model can be a potential solution to the cooking state recognition problem.