Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection

Ummadisingu, Avinash; Takahashi, Kuniyuki; Fukaya, Naoki

doi:10.1109/icra46639.2022.9812448

Cited by 8 publications

(1 citation statement)

References 36 publications

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“…As one key task in food computing, food detection aims to locate and recognize food objects simultaneously [1,23,29]. Food detection can enable various applications such as food recommendation, dietary assessment, and robotics control [27,45,48]. However, it is challenging to detect food objects under real-world scenarios due to the constant emergence of novel food classes, such as the continued updates of food categories in restaurants [40].…”

Section: Introductionmentioning

confidence: 99%

SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection

Zhou,

Min,

Zhang

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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Food detection is becoming a fundamental task in food computing that supports various multimedia applications, including food recommendation and dietary monitoring. To deal with real-world scenarios, food detection needs to localize and recognize novel food objects that are not seen during training, demanding Zero-Shot Detection (ZSD). However, the complexity of semantic attributes and intra-class feature diversity poses challenges for ZSD methods in distinguishing fine-grained food classes. To tackle this, we propose the Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food Detection (ZSFD). SeeDS consists of two modules: a Semantic Separable Synthesizing Module (S 3 M) and a Region Feature Denoising Diffusion Model (RFDDM). The S 3 M learns the disentangled semantic representation for complex food attributes from ingredients and cuisines, and synthesizes discriminative food features via enhanced semantic information. The RFDDM utilizes a novel diffusion model to generate diversified region features and enhances ZSFD via fine-grained synthesized features. Extensive experiments show the state-of-the-art ZSFD performance of our proposed method on two food datasets, ZSFooD and UECFOOD-256. Moreover, SeeDS also maintains effectiveness on general ZSD * Corresponding author.

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Section: Introductionmentioning

confidence: 99%

SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection

Zhou,

Min,

Zhang

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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A Survey of Synthetic Data Augmentation Methods in Machine Vision

Mumuni,

Gerrar

2024

Mach. Intell. Res.

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Vision-based food handling system for high-resemblance random food items

Zeng,

Teoh,

Zhu

et al. 2024

Robotica

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The rise in the number of automated robotic kitchens accelerated the need for advanced food handling system, emphasizing food analysis including ingredient classification pose recognition and assembling strategy. Selecting the optimal piece from a pile of similarly shaped food items is a challenge to automated meal assembling system. To address this, we present a constructive assembling algorithm, introducing a unique approach for food pose detection–Fast Image to Pose Detection (FI2PD), and a closed-loop packing strategy. Powered by a convolutional neural network (CNN) and a pose retrieval model, FI2PD is adept at constructing a 6D pose from only RGB images. The method employs a coarse-to-fine approach, leveraging the CNN to pinpoint object orientation and position, alongside a pose retrieval process for target selection and 6D pose derivation. Our closed-loop packing strategy, aided by the Item Arrangement Verifier, ensures precise arrangement and system robustness. Additionally, we introduce our FdIngred328 dataset of nine food categories ranging from fake foods to real foods, and the automatically generated data based on synthetic techniques. The performance of our method for object recognition and pose detection has been demonstrated to achieve a success rate of 97.9%. Impressively, the integration of a closed-loop strategy into our meal-assembly process resulted in a notable success rate of 90%, outperforming the results of systems lacking the closed-loop mechanism.

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Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection

Cited by 8 publications

References 36 publications

SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection

SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection

A Survey of Synthetic Data Augmentation Methods in Machine Vision

Vision-based food handling system for high-resemblance random food items

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