Grasp Point Calculation and Food Waste Detection for Dish-recycling Robot

Ishibashi, Ryuto; Kaneko, Hayata; Yue, Xuebin; Meng, Lin

doi:10.1109/icamechs57222.2022.10003459

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Cited by 3 publications

References 19 publications

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Machine Learning Based Hardware Accelerated Cobot Grasping in the Food Industry

Ivačko,

Ćirić,

Cojbašić

et al. 2024

2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)

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Machine Learning Based Hardware Accelerated Cobot Grasping in the Food Industry

Ivačko,

Ćirić,

Cojbašić

et al. 2024

2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)

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YOLO-MSA: A Multiscale Stereoscopic Attention Network for Empty-Dish Recycling Robots

Yue,

Meng

2023

IEEE Trans. Instrum. Meas.

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Model Compression for Deep Neural Networks: A Survey

Meng

2023

Computers

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Currently, with the rapid development of deep learning, deep neural networks (DNNs) have been widely applied in various computer vision tasks. However, in the pursuit of performance, advanced DNN models have become more complex, which has led to a large memory footprint and high computation demands. As a result, the models are difficult to apply in real time. To address these issues, model compression has become a focus of research. Furthermore, model compression techniques play an important role in deploying models on edge devices. This study analyzed various model compression methods to assist researchers in reducing device storage space, speeding up model inference, reducing model complexity and training costs, and improving model deployment. Hence, this paper summarized the state-of-the-art techniques for model compression, including model pruning, parameter quantization, low-rank decomposition, knowledge distillation, and lightweight model design. In addition, this paper discusses research challenges and directions for future work.

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Grasp Point Calculation and Food Waste Detection for Dish-recycling Robot

Cited by 3 publications

References 19 publications

Machine Learning Based Hardware Accelerated Cobot Grasping in the Food Industry

Machine Learning Based Hardware Accelerated Cobot Grasping in the Food Industry

YOLO-MSA: A Multiscale Stereoscopic Attention Network for Empty-Dish Recycling Robots

Model Compression for Deep Neural Networks: A Survey

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