Opto-Electronic Engineering, Volume. 52, Issue 5, 240265(2025)

Anchor-free instance segmentation algorithm based on YOLACTR

Ting Mei1,2, Jingwei Zhao1,2, Shanling Lin2,3、*, Ziyu Xie1,2, Zhixian Lin1,2,3, and Tailiang Guo1,2
Author Affiliations
  • 1College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350116, China
  • 2Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian 350108, China
  • 3School of Advanced Manufacturing, Fuzhou University, Quanzhou, Fujian 362200, China
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    Figures & Tables(19)
    YOLACT structure diagram
    YOLACTR network structure
    Anchless anchor box example segmentation model structure
    Schematic diagram of the positional embedding
    Structure diagram of mask generation network
    Predictive network structure
    Object detection results
    Instance segmentation detection results
    Loss variation curves under different loss function configurations
    Different curves for 2- and 6-layer Transformer models. (a) Loss variation curves; (b) Accuracy variation curves
    Schematic diagrams of the decline of each loss during the training process. (a) Total loss curve; (b) Classification loss curve; (c) Mask loss curve
    Mask detection accuracy rise graph
    Comparison diagrams before improvement (left) and after improvement (right)
    Comparison diagram of instance segmentation results 1
    Comparison diagram of instance segmentation results 2
    • Table 1. Experimental environment configuration

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      Table 1. Experimental environment configuration

      Operating systemFrameworkCPUGPUMemoryVideo memoryPythonCUDNNCUDA
      Ubuntu 20.04.3 LTSPytorchAMD EPYC 7601NVIDIA GeForce RTX 3090 × 232 GB48 GB3.8.108.0.511.0
    • Table 2. Segmentation results under different loss function configurations

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      Table 2. Segmentation results under different loss function configurations

      Loss function configurationAP/%AP50/%AP75/%
      Replace dice loss3.04.83.6
      Replace focal loss11.523.810.2
      Dice loss + focal loss12.726.910.9
    • Table 3. Segmentation results with different numbers of Transformer layers

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      Table 3. Segmentation results with different numbers of Transformer layers

      Transformer layersAP/%AP50/%AP75/%APS/%APM/%APL/%
      2 layers12.726.910.91.05.128.9
      6 layers14.129.312.42.06.634.7
    • Table 4. Instance segmentation results on the COCO dataset

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      Table 4. Instance segmentation results on the COCO dataset

      Network modelAP/%AP50/%AP75/%APS/%APM/%APL/%
      YOLACT28.046.229.18.930.247.0
      Mask R-CNN30.551.132.114.234.143.1
      YOLACTR29.148.730.010.231.446.8
      PolarMask[28]30.451.931.013.432.442.8
      SOLO33.153.535.012.236.150.8
      QueryInst37.558.740.518.440.257.2
      Mask2Former42.965.346.022.146.364.8
      Proposed algorithm35.255.437.512.238.057.3
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    Ting Mei, Jingwei Zhao, Shanling Lin, Ziyu Xie, Zhixian Lin, Tailiang Guo. Anchor-free instance segmentation algorithm based on YOLACTR[J]. Opto-Electronic Engineering, 2025, 52(5): 240265

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    Paper Information

    Category: Article

    Received: Nov. 12, 2024

    Accepted: Apr. 8, 2025

    Published Online: Jul. 18, 2025

    The Author Email: Shanling Lin (林珊玲)

    DOI:10.12086/oee.2025.240265

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