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Stacked Garbage Instance Segmentation Algorithm Based on TLF-YOLOv8
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Li LI1, 2, 3, Jing LIANG1, 2, Xu-dong CHEN1, 2, Hong-Guang PAN1, 2, Fa-rong KOU1, 3
Science Technology and Engineering | 2025, 25(5) : 2009 - 2018
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Science Technology and Engineering | 2025, 25(5): 2009-2018
Papers·Automation and Computational Technology
Stacked Garbage Instance Segmentation Algorithm Based on TLF-YOLOv8
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Li LI1, 2, 3, Jing LIANG1, 2, Xu-dong CHEN1, 2, Hong-Guang PAN1, 2, Fa-rong KOU1, 3
Affiliations
  • 1 School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
  • 2 Xi’an Key Laboratory of Electrical Equipment Status Monitoring and Power Supply Safety, Xi’an 710054, China
  • 3 School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Published: 2025-02-18 doi: 10.12404/j.issn.1671-1815.2402281
Outline
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Compared to image instance segmentation in general scenes, instance segmentation in complex stacked scenes is affected by complex situations such as severe occlusion and stacking of similar objects, making instance segmentation more difficult. To solve the problem of garbage instance segmentation in complex stacking scenarios, an instance segmentation algorithm combining YOLOv8 and two-layer feature network strategy was proposed. Firstly, the feature data was layered in the data preprocessing part, and the two-branch feature fusion was realized through the graph convolutions network (GCN), which reduces the influence of stacking on the features of the occluded objects, thus solving the instance segmentation problem under complex stack occlusion. At the same time, in order to solve the problem that similar objects are easily confused, a soft threshold non-maximum suppression algorithm and a new intersection ratio algorithm were integrated. Finally, according to the complexity of application scenarios and data sets, the feature extraction module of the backbone network was optimized, and the multi-scale attention mechanism was introduced in the backbone network, which effectively improves the detection performance of the model. In the experiment, examples of occlusive garbage classification were used to segment the dataset. The experimental results show that this method outperforms other methods in terms of average accuracy, average accuracy when the intersection to union ratio threshold is 0.5 (AP50), and average accuracy when the intersection to union ratio is 0.5~0.95 (AP50~95). Compared with the original YOLOv8 algorithm, the detection AP50is increased by 7.9% and the segmentation AP50 is increased by 5.4%, which has better detection and segmentation effects.

garbage stacking  /  double layer feature decoupling fusion  /  YOLOv8 algorithm  /  soft non-maximum suppression  /  wise-intersection over union  /  expectation-maximization attention
Li LI, Jing LIANG, Xu-dong CHEN, Hong-Guang PAN, Fa-rong KOU. Stacked Garbage Instance Segmentation Algorithm Based on TLF-YOLOv8[J]. Science Technology and Engineering, 2025 , 25 (5) : 2009 -2018 . DOI: 10.12404/j.issn.1671-1815.2402281
Year 2025 volume 25 Issue 5
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doi: 10.12404/j.issn.1671-1815.2402281
  • Receive Date:2024-03-31
  • Online Date:2025-07-29
  • Published:2025-02-18
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  • Received:2024-03-31
  • Revised:2024-11-18
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Affiliations
    1 School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    2 Xi’an Key Laboratory of Electrical Equipment Status Monitoring and Power Supply Safety, Xi’an 710054, China
    3 School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
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表12种不同金属材料的力学参数

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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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