Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML Models
This study explores the application of advanced artificial intelligence based preprocessing techniques to improve semantic forestry segmentation. The research investigates the performance of You Only Look Once version 8 (YOLOv8) from Ultralytics, Detectron2 from Meta, and the Segment Anything Model...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11020665/ |
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| author | Krzysztof Wolk Jacek Niklewski Michal Kopczynski Marek S. Tatara Oleg Zero |
| author_facet | Krzysztof Wolk Jacek Niklewski Michal Kopczynski Marek S. Tatara Oleg Zero |
| author_sort | Krzysztof Wolk |
| collection | DOAJ |
| description | This study explores the application of advanced artificial intelligence based preprocessing techniques to improve semantic forestry segmentation. The research investigates the performance of You Only Look Once version 8 (YOLOv8) from Ultralytics, Detectron2 from Meta, and the Segment Anything Model (SAM) from Meta, with the goal of enhancing segmentation accuracy and detail in forest environments. Goal is accomplished by combining models into a pipeline along with machine-larning-based preprocessing models. The integration of SAM and pre-processing steps significantly enhanced the quality and number of segmentation masks, resulting in more accurate and detailed representations of complex forest structures compared to the ground truth. The findings highlight the importance of combining AI models with effective preprocessing strategies to navigate the complexities of forest environments, offering valuable insights for future advancements in semantic forestry segmentation. |
| format | Article |
| id | doaj-art-4068fb28662a48fb8e00e5a1be7a583f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4068fb28662a48fb8e00e5a1be7a583f2025-08-20T03:21:00ZengIEEEIEEE Access2169-35362025-01-0113986029862110.1109/ACCESS.2025.357571311020665Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML ModelsKrzysztof Wolk0https://orcid.org/0000-0001-5030-334XJacek Niklewski1Michal Kopczynski2Marek S. Tatara3Oleg Zero4DAC.digital SA, Gdańsk, PolandDAC.digital SA, Gdańsk, PolandDAC.digital SA, Gdańsk, PolandDAC.digital SA, Gdańsk, PolandDAC.digital SA, Gdańsk, PolandThis study explores the application of advanced artificial intelligence based preprocessing techniques to improve semantic forestry segmentation. The research investigates the performance of You Only Look Once version 8 (YOLOv8) from Ultralytics, Detectron2 from Meta, and the Segment Anything Model (SAM) from Meta, with the goal of enhancing segmentation accuracy and detail in forest environments. Goal is accomplished by combining models into a pipeline along with machine-larning-based preprocessing models. The integration of SAM and pre-processing steps significantly enhanced the quality and number of segmentation masks, resulting in more accurate and detailed representations of complex forest structures compared to the ground truth. The findings highlight the importance of combining AI models with effective preprocessing strategies to navigate the complexities of forest environments, offering valuable insights for future advancements in semantic forestry segmentation.https://ieeexplore.ieee.org/document/11020665/Convolutional neural networksforestrymachine learningobject detectionpreprocessingsemantic segmentation |
| spellingShingle | Krzysztof Wolk Jacek Niklewski Michal Kopczynski Marek S. Tatara Oleg Zero Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML Models IEEE Access Convolutional neural networks forestry machine learning object detection preprocessing semantic segmentation |
| title | Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML Models |
| title_full | Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML Models |
| title_fullStr | Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML Models |
| title_full_unstemmed | Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML Models |
| title_short | Enhancing Semantic Forestry Segmentation Through Advanced Preprocessing With ML Models |
| title_sort | enhancing semantic forestry segmentation through advanced preprocessing with ml models |
| topic | Convolutional neural networks forestry machine learning object detection preprocessing semantic segmentation |
| url | https://ieeexplore.ieee.org/document/11020665/ |
| work_keys_str_mv | AT krzysztofwolk enhancingsemanticforestrysegmentationthroughadvancedpreprocessingwithmlmodels AT jacekniklewski enhancingsemanticforestrysegmentationthroughadvancedpreprocessingwithmlmodels AT michalkopczynski enhancingsemanticforestrysegmentationthroughadvancedpreprocessingwithmlmodels AT marekstatara enhancingsemanticforestrysegmentationthroughadvancedpreprocessingwithmlmodels AT olegzero enhancingsemanticforestrysegmentationthroughadvancedpreprocessingwithmlmodels |