SCTNet-NAS: efficient semantic segmentation via neural architecture search for cloud-edge collaborative perception
Abstract Efficient semantic segmentation on edge devices is critical for cloud-edge perception, yet achieving high accuracy under resource constraints remains challenging. To alleviate this problem, this paper presents SCTNet-NAS, a novel framework for efficient edge segmentation via cloud-edge co-o...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01996-5 |
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| author | Ruyu Liu Lin Wang Zhihao Yu Haoyu Zhang Xiufeng Liu Bo Sun Xv Huo Jianhua Zhang |
| author_facet | Ruyu Liu Lin Wang Zhihao Yu Haoyu Zhang Xiufeng Liu Bo Sun Xv Huo Jianhua Zhang |
| author_sort | Ruyu Liu |
| collection | DOAJ |
| description | Abstract Efficient semantic segmentation on edge devices is critical for cloud-edge perception, yet achieving high accuracy under resource constraints remains challenging. To alleviate this problem, this paper presents SCTNet-NAS, a novel framework for efficient edge segmentation via cloud-edge co-optimization. SCTNet-NAS that unifies multi-objective neural architecture search (NAS), feature-based knowledge distillation from a cloud-based vision transformer (ViT) teacher, and specialized decoder design to simultaneously deliver high accuracy and real-time efficiency for semantic segmentation on resource-constrained edge devices. The method first constructs a weight-sharing supernet and applies an non-dominated sorting genetic algorithm (NSGA-II) to explore candidate encoders in a single forward pass, then transfers global context from a vision transformer teacher to each candidate via the VitGuidance feature-level distillation scheme. In addition, our meticulously designed SCTHead and AU_SCTHead decoder modules add adaptive channel re-calibration to enhance segmentation performance and refined boundary delineation. Evaluation demonstrates SCTNet-NAS achieves a significantly enhanced accuracy-efficiency trade-off versus state-of-the-art methods, enabling high-performance edge AI perception. |
| format | Article |
| id | doaj-art-8a6de3a3609149a7b1c1f34933681f46 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-8a6de3a3609149a7b1c1f34933681f462025-08-20T04:02:45ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-06-0111811610.1007/s40747-025-01996-5SCTNet-NAS: efficient semantic segmentation via neural architecture search for cloud-edge collaborative perceptionRuyu Liu0Lin Wang1Zhihao Yu2Haoyu Zhang3Xiufeng Liu4Bo Sun5Xv Huo6Jianhua Zhang7School of Information Science and Technology, Hangzhou Normal UniversitySchool of Information Science and Technology, Hangzhou Normal UniversitySchool of Information Science and Technology, Hangzhou Normal UniversitySchool of Information Science and Technology, Hangzhou Normal UniversityDepartment of Technology, Management and Economics, Technical University of DenmarkHaixi Institutes, Chinese Academy of Sciences, Quanzhou Institute of Equipment ManufacturingSchool of Mathematics and Computer Science, Zhejiang A and F UniversitySchool of Computer Science and Engineering, Tianjin University of TechnologyAbstract Efficient semantic segmentation on edge devices is critical for cloud-edge perception, yet achieving high accuracy under resource constraints remains challenging. To alleviate this problem, this paper presents SCTNet-NAS, a novel framework for efficient edge segmentation via cloud-edge co-optimization. SCTNet-NAS that unifies multi-objective neural architecture search (NAS), feature-based knowledge distillation from a cloud-based vision transformer (ViT) teacher, and specialized decoder design to simultaneously deliver high accuracy and real-time efficiency for semantic segmentation on resource-constrained edge devices. The method first constructs a weight-sharing supernet and applies an non-dominated sorting genetic algorithm (NSGA-II) to explore candidate encoders in a single forward pass, then transfers global context from a vision transformer teacher to each candidate via the VitGuidance feature-level distillation scheme. In addition, our meticulously designed SCTHead and AU_SCTHead decoder modules add adaptive channel re-calibration to enhance segmentation performance and refined boundary delineation. Evaluation demonstrates SCTNet-NAS achieves a significantly enhanced accuracy-efficiency trade-off versus state-of-the-art methods, enabling high-performance edge AI perception.https://doi.org/10.1007/s40747-025-01996-5Semantic segmentationNeural architecture searchKnowledge distillationEdge computingCloud-edge collaboration |
| spellingShingle | Ruyu Liu Lin Wang Zhihao Yu Haoyu Zhang Xiufeng Liu Bo Sun Xv Huo Jianhua Zhang SCTNet-NAS: efficient semantic segmentation via neural architecture search for cloud-edge collaborative perception Complex & Intelligent Systems Semantic segmentation Neural architecture search Knowledge distillation Edge computing Cloud-edge collaboration |
| title | SCTNet-NAS: efficient semantic segmentation via neural architecture search for cloud-edge collaborative perception |
| title_full | SCTNet-NAS: efficient semantic segmentation via neural architecture search for cloud-edge collaborative perception |
| title_fullStr | SCTNet-NAS: efficient semantic segmentation via neural architecture search for cloud-edge collaborative perception |
| title_full_unstemmed | SCTNet-NAS: efficient semantic segmentation via neural architecture search for cloud-edge collaborative perception |
| title_short | SCTNet-NAS: efficient semantic segmentation via neural architecture search for cloud-edge collaborative perception |
| title_sort | sctnet nas efficient semantic segmentation via neural architecture search for cloud edge collaborative perception |
| topic | Semantic segmentation Neural architecture search Knowledge distillation Edge computing Cloud-edge collaboration |
| url | https://doi.org/10.1007/s40747-025-01996-5 |
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