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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.
Hai-li et al. (Wed,) studied this question.