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Research on Stacked Part Instance Segmentation Based on Improved YOLOv8
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    Abstract:

    In order to achieve rapid recognition and selection of stacked parts by robots in complex industrial environments,an improved YOLOv8s instance segmentation model was constructed and applied to real-time recognition and segmentation of stacked parts.To address the issue of difficult segmentation of stacked industrial parts,the original model′s backbone network was replaced with a PoolFormer backbone network with stronger feature extraction capabilities to improve the edge segmentation effect of stacked parts;in order to better filter out excess background information and retain key information,a better CARAFE upsampling module was introduced.The experimental results show that the average segmentation accuracy and prediction box accuracy of the improved model are 93.57% and 97.47%,respectively,which are 1.89% and 1.23% higher than the original model,and far higher than the YOLACT++and SOLOv2 models with the same type,verifying the effectiveness of the improved model.

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王众玄,邹光明,顾浩文,许艳涛,李陈佳瑞.基于改进YOLOv8的堆叠零件实例分割研究[J].机床与液压英文版,2024,52(19):9-16.

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  • Online: October 23,2024
  • Published: October 15,2024
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