Object detection based on improved RT-DETR for human-robot collaboration manufacturing system

Haili Lv; Qi Xiang; Jinhua Xiao

doi:10.1117/12.3030128

5 June 2024 Object detection based on improved RT-DETR for human-robot collaboration manufacturing system

Haili Lv, Qi Xiang, Jinhua Xiao

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131638P (2024) https://doi.org/10.1117/12.3030128
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

In modern intelligent manufacturing, human-robot collaboration is essential for combining the advantages of robots and human to facilitate mass customized production. In order to improve robot's understanding of the operator's movements and the working environment, an intelligent recognition method based on improved RT-DETR is proposed. The method introduces CBAM modules before the multi-scale recognition layer to improve the recognition accuracy of the model. Meanwhile, in order to cope with the problem of increased number of parameters and computation complexity caused by the addition of the CBAM modules, the Conv module of the backbone network is replaced by the GhostConv module. Experimental results show that these two enhanced methods (CBAM and GhostConv) effectively improve the detection performance of the original RT-DETR model under the dataset examined.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haili Lv, Qi Xiang, and Jinhua Xiao "Object detection based on improved RT-DETR for human-robot collaboration manufacturing system", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131638P (5 June 2024); https://doi.org/10.1117/12.3030128

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KEYWORDS

Object detection

Robots

Manufacturing

Target detection

Data modeling

Deep learning

Performance modeling

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