End-To-End Skeletal Point Detection Algorithm for Garment Processing Actions

The standardization of garment processing actions not only assists enterprises to better manage the processing and production process, but also can effectively improve the production technology of workers, ensure product stability, and further improve the competitiveness of enterprises. In this paper, an end-to-end skeleton point detection algorithm for garment processing actions is proposed. Firstly, the HRNet algorithm with high representation was improved, and the characterization modules were rearranged on the basis of maintaining the parallel operation of the HRNet network. Secondly, combined with object detection and multi-target tracking, the bone point feature detection of garment processing actions was realized, and finally, on the 12,600 video datasets with human targets, the simulation verification is carried out, and mAP and OKS are used as the evaluation indicators of the bone point detection effect, and a large number of experimental results show that end-to-end skeleton point detection algorithm based on garment processing action proposed in this paper can effectively detect the bone point features of garment processing action.