Instance Segmentation Method based on R2SC-Yolact++

To address the problems of missed detection, segmentation error and poor target edge segmentation in the instance segmentation model, a R2SC-Yolact++ instance segmentation approach based on the improved Yolact++ is proposed. Firstly, the backbone network adopts Res2Net which introduces spatial attention mechanism (SAM) to improve the problem of segmentation error by better extracting feature information; then, high-quality masks are obtained by fusing the detail information of the shallow feature P2 as the input to the prototype mask branch; finally, the problem of missed detection was solved by introducing Cluster-NMS in order to improve the accuracy of the detection boxes. In order to illustrate the effectiveness of the improved model, experiments were conducted on two publicly available datasets, the COCO and CVPPP datasets. The experimental results show that the accuracy on the COCO dataset is 1.1% higher than the original model. And the accuracy on the CVPPP dataset is 1.7% better than before the improvement, which is better than other mainstream instance segmentation algorithms such as Mask RCNN. Finally, the improved model is applied to the insulator dataset, which can segment the shed of insulator accurately.

@article{Ma2023,
  title     = {Instance Segmentation Method based on R2SC-Yolact++},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Liqun Ma and Chuang Cai and Haonan Xie and Xuanxuan Fan and Zhijian Qu and Chongguang Ren},
  doi       = {10.14569/IJACSA.2023.0141058},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141058}
}