Robust deep-learning based refrigerator food recognition.

Abstract

Automatic food identification utilizing artificial intelligence (AI) technology in smart refrigerators presents an innovative solution. However, existing studies exhibit significant limitations. Achieving consistent high performance in recognition across varying camera distances and diverse real-world conditions remain a formidable challenge. Current approaches often struggle to accurately recognize items in scenarios involving occlusions, variable distortions, and complex backgrounds, thereby limiting their practical applicability in household environments. This study addresses these deficiencies by enhancing the Feature Pyramid Network (FPN) of YOLACT with an additional layer designed to capture nuanced information. Furthermore, we propose a two-stage data augmentation method that simulates diverse conditions including distortion and occlusion, to generate images that reflect various backgrounds and handheld scenarios. Comparative analyses with previous research and evaluations on our original dataset demonstrate that our approach significantly improves recognition rates for both typical and challenging real-world images. These enhancements contribute to more effective food waste management in households and indicate broader applications for automatic identification systems.