Instance Segmentation Method Based on DPA-SOLOV2

To solve the problems of missed detection, segmentation errors in instance segmentation models, we propose an instance segmentation approach, DPA-SOLOV2, based on the improved segmenting objects by locations V2 (SOLO V2). Firstly, DPA-SOLOV2 introduces deformable convolutional networks (DCN) into the feature extraction network ResNet50. By freely sampling points to convolve features of any shape, the network can extract feature information more effectively. Secondly, DPA-SOLOV2 uses the path aggregation feature pyramid network (PAFPN) feature fusion method to replace the feature pyramid. By adding a bottom-up path, it can better transmit the location information of features and also enhance the information interaction between features. To prove the effectiveness of the improved model, we conduct experiments on two public datasets, COCO and CVPPP. The experimental results show that the accuracy of the improved model on the COCO dataset is 1.3% higher than that of the original model, and the accuracy on the CVPPP dataset is 1.5% higher than that before the improvement. Finally, the improved model is applied to the insulator dataset, which can accurately segment the umbrella skirt of insulators and outperforms other mainstream instance segmentation algorithms such as Yolact++.

@article{Feng2025,
  title     = {Instance Segmentation Method Based on DPA-SOLOV2},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yuyue Feng and Liqun Ma and Yinbao Xie and Zhijian Qu},
  doi       = {10.14569/IJACSA.2025.0160528},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160528}
}