Differential Evolution Integrated Hybrid Deep Learning Model for Object Detection in Pre-made Dishes

TL;DR

This paper introduces a novel hybrid deep learning model, DEIHDL, that combines multiple YOLO and transformer-based models with differential evolution optimization to improve object detection accuracy in complex pre-made dish scenes.

Contribution

The paper presents a new DEIHDL model that integrates diverse deep learning models with differential evolution for optimized object detection in challenging environments.

Findings

DEIHDL significantly outperforms individual base models in accuracy.

The integration method improves detection in occluded and low-light conditions.

Weighted boxes fusion enhances confidence scoring during model combination.

Abstract

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.