Differential Evolution Integrated Hybrid Deep Learning Model for Object Detection in Pre-made Dishes
This work addresses a domain-specific problem for the pre-made dishes industry, where accurate object detection is crucial for ingredient selection and quality evaluation, but it is incremental as it builds on existing YOLO and transformer-based methods with integration techniques.
The paper tackles the problem of accurate object detection in pre-made dishes, which is challenging due to overlapping occlusion, ingredient similarity, and poor lighting, by proposing a Differential Evolution Integrated Hybrid Deep Learning (DEIHDL) model that integrates three base models with optimized weights, and it demonstrates significant performance improvements over base models in experiments on real datasets.
With the continuous improvement of people's living standards and fast-paced working conditions, pre-made dishes are becoming increasingly popular among families and restaurants due to their advantages of time-saving, convenience, variety, cost-effectiveness, standard quality, etc. Object detection is a key technology for selecting ingredients and evaluating the quality of dishes in the pre-made dishes industry. To date, many object detection approaches have been proposed. However, accurate object detection of pre-made dishes is extremely difficult because of overlapping occlusion of ingredients, similarity of ingredients, and insufficient light in the processing environment. As a result, the recognition scene is relatively complex and thus leads to poor object detection by a single model. To address this issue, this paper proposes a Differential Evolution Integrated Hybrid Deep Learning (DEIHDL) model. The main idea of DEIHDL is three-fold: 1) three YOLO-based and transformer-based base models are developed respectively to increase diversity for detecting objects of pre-made dishes, 2) the three base models are integrated by differential evolution optimized self-adjusting weights, and 3) weighted boxes fusion strategy is employed to score the confidence of the three base models during the integration. As such, DEIHDL possesses the multi-performance originating from the three base models to achieve accurate object detection in complex pre-made dish scenes. Extensive experiments on real datasets demonstrate that the proposed DEIHDL model significantly outperforms the base models in detecting objects of pre-made dishes.