A rapid oriented detection method of virtual components for augmented assembly

Augmented assembly provides intuitive assembly guidelines to the assembly operators. With assembly tasks developing toward diversification, the position of virtual components that are superimposed on the real environment may change frequently and virtual components have different length-diameter ratios and overlap each other. However, existing object detection methods are time consuming and hardly separate virtual components in dense for augmented assembly. Therefore, this paper proposes a rapid oriented detection method based on swift oriented detection region -based convolutional network (SODR-CNN) for virtual components in augmented assembly. Specifically, a dual channel selection (DCS) structure is designed to extract spatial features by reducing redundant computation and memory access. Further, the novel backbone network, rapid network (RapidNet), is proposed building upon the DCS, which can decrease the inference latency of SODR-CNN. Finally, the comprehensive experiments demonstrate that the RapidNet achieves lower inference latency than comparison backbone networks, which means that the SODR-CNN achieves superior inference latency and detection accuracy. Moreover, intuitive evaluation experiments are carried out, in which the effectiveness of the proposed method is further verified by searching for several virtual components in a real environment.