Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater Images
Numerous methods have been proposed for semantic segmentation and the state-of-the-art part is likely to be incorporated by deep learning-based methods which show a salient performance. This study addresses the challenge of semantic segmentation in low-contrast imbalanced underwater images. Moreover...
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MDPI AG
2024-12-01
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| author | Jale Bektaş |
| author_facet | Jale Bektaş |
| author_sort | Jale Bektaş |
| collection | DOAJ |
| description | Numerous methods have been proposed for semantic segmentation and the state-of-the-art part is likely to be incorporated by deep learning-based methods which show a salient performance. This study addresses the challenge of semantic segmentation in low-contrast imbalanced underwater images. Moreover, it employs nine model fusions as a downstream workflow task using encoder–decoder architectures with Dice Loss and Focal Loss training focusing on the imbalance data. Afterwards, the most effective two encoder–decoder fusion models, Res34+Unet and VGG19+FPN, by 0.592%, 0.590% mIoU on average and by 0.510%, 0.491% F1-score yielded better performance, respectively, than other models. Using a weight-optimization algorithm, the ensemble model with recreated IoU results improves the accuracy for both the Res34+Unet and the VGG19+FPN models, by 0.652% mIoU on average which is 6%. The ensemble model combines the model performances of independent models by considering their superior inference accuracy on a per-class basis separately and improves the model performances by emphasizing the better one on a per-class basis. |
| format | Article |
| id | doaj-art-cdede0e443184f07b354d5b6aec94d59 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-cdede0e443184f07b354d5b6aec94d592025-08-20T02:00:56ZengMDPI AGApplied Sciences2076-34172024-12-0114241196410.3390/app142411964Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater ImagesJale Bektaş0Department of Computer Engineering, Mersin University, 33110 Mersin, TürkiyeNumerous methods have been proposed for semantic segmentation and the state-of-the-art part is likely to be incorporated by deep learning-based methods which show a salient performance. This study addresses the challenge of semantic segmentation in low-contrast imbalanced underwater images. Moreover, it employs nine model fusions as a downstream workflow task using encoder–decoder architectures with Dice Loss and Focal Loss training focusing on the imbalance data. Afterwards, the most effective two encoder–decoder fusion models, Res34+Unet and VGG19+FPN, by 0.592%, 0.590% mIoU on average and by 0.510%, 0.491% F1-score yielded better performance, respectively, than other models. Using a weight-optimization algorithm, the ensemble model with recreated IoU results improves the accuracy for both the Res34+Unet and the VGG19+FPN models, by 0.652% mIoU on average which is 6%. The ensemble model combines the model performances of independent models by considering their superior inference accuracy on a per-class basis separately and improves the model performances by emphasizing the better one on a per-class basis.https://www.mdpi.com/2076-3417/14/24/11964semantic segmentationencoder–decoder fusionUnetFPNLinkNetensemble model |
| spellingShingle | Jale Bektaş Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater Images Applied Sciences semantic segmentation encoder–decoder fusion Unet FPN LinkNet ensemble model |
| title | Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater Images |
| title_full | Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater Images |
| title_fullStr | Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater Images |
| title_full_unstemmed | Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater Images |
| title_short | Automating an Encoder–Decoder Incorporated Ensemble Model: Semantic Segmentation Workflow on Low-Contrast Underwater Images |
| title_sort | automating an encoder decoder incorporated ensemble model semantic segmentation workflow on low contrast underwater images |
| topic | semantic segmentation encoder–decoder fusion Unet FPN LinkNet ensemble model |
| url | https://www.mdpi.com/2076-3417/14/24/11964 |
| work_keys_str_mv | AT jalebektas automatinganencoderdecoderincorporatedensemblemodelsemanticsegmentationworkflowonlowcontrastunderwaterimages |