EFCRFNet: A novel multi-scale framework for salient object detection
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| 出版年: | PLoS One vol. 20, no. 5 (May 2025), p. e0323757 |
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| 第一著者: | |
| その他の著者: | , , |
| 出版事項: |
Public Library of Science
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1371/journal.pone.0323757 |2 doi | |
| 035 | |a 3206832778 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Peng, Hong | |
| 245 | 1 | |a EFCRFNet: A novel multi-scale framework for salient object detection | |
| 260 | |b Public Library of Science |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Salient Object Detection (SOD) is a fundamental task in computer vision, aiming to identify prominent regions within images. Traditional methods and deep learning-based models often encounter challenges in capturing crucial information in complex scenes, particularly due to inadequate edge feature extraction, which compromises the precise delineation of object contours and boundaries. To address these challenges, we introduce EFCRFNet, a novel multi-scale feature extraction model that incorporates two innovative modules: the Enhanced Conditional Random Field (ECRF) and the Edge Feature Enhancement Module (EFEM). The ECRF module leverages advanced spatial attention mechanisms to enhance multimodal feature fusion, enabling robust detection in complex environments. Concurrently, the EFEM module focuses on refining edge features to strengthen multi-scale feature representation, significantly improving boundary recognition accuracy. Extensive experiments on standard benchmark datasets demonstrate that EFCRFNet achieves notable performance gains across key evaluation metrics, including MAE (0.64%), Fm (1.04%), Em (8.73%), and Sm (7.4%). These results underscore the effectiveness of EFCRFNet in enhancing detection accuracy and optimizing feature fusion, advancing the state of the art in salient object detection. | |
| 653 | |a Innovations | ||
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Conditional random fields | ||
| 653 | |a Salience | ||
| 653 | |a Computer vision | ||
| 653 | |a Modules | ||
| 653 | |a Algorithms | ||
| 653 | |a Object recognition | ||
| 653 | |a Localization | ||
| 653 | |a Efficiency | ||
| 653 | |a Semantics | ||
| 653 | |a Thermography | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Hu, Yunfei | |
| 700 | 1 | |a Yu, Baocai | |
| 700 | 1 | |a Zhang, Zhen | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 5 (May 2025), p. e0323757 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3206832778/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3206832778/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3206832778/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |