SAEC: Scene-Aware Enhanced Edge-Cloud Collaborative Industrial Vision Inspection with Multimodal LLM
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915506070487040 |
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| author | Tian, Yuhao Yang, Zheming |
| author_facet | Tian, Yuhao Yang, Zheming |
| contents | Industrial vision inspection requires high accuracy under stringent resource constraints, yet existing approaches face a fundamental trade-off. Multimodal LLMs (MLLMs) deliver strong reasoning capabilities but incur prohibitive computational costs, while lightweight edge models often fail on complex cases. In this paper, we present SAEC, a scene-aware enhanced edge-cloud collaborative industrial vision inspection framework with MLLM. The framework is composed of three synergistic components: (1) Efficient MLLM Fine-Tuning for Complex Defect Inspection, (2) Lightweight Multiscale Scene-Complexity Estimation, and (3) Adaptive Edge-Cloud Scheduler. Together, these modules enable robust defect detection by tailoring multimodal reasoning to scene complexity and dynamically balancing computation between edge and cloud resources. Experimental results on MVTec AD and KSDD2 datasets demonstrate that SAEC attains 85.11% and 82.72% accuracy, surpassing Qwen by 22.1% and 20.8%, and LLaVA by 33.3% and 31.6%. It also reduces runtime by up to 22.4% and cuts energy per correct decision by 40%-74%. The code is available at https://github.com/YuHao-Tian/SAEC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17136 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAEC: Scene-Aware Enhanced Edge-Cloud Collaborative Industrial Vision Inspection with Multimodal LLM Tian, Yuhao Yang, Zheming Computer Vision and Pattern Recognition Artificial Intelligence Industrial vision inspection requires high accuracy under stringent resource constraints, yet existing approaches face a fundamental trade-off. Multimodal LLMs (MLLMs) deliver strong reasoning capabilities but incur prohibitive computational costs, while lightweight edge models often fail on complex cases. In this paper, we present SAEC, a scene-aware enhanced edge-cloud collaborative industrial vision inspection framework with MLLM. The framework is composed of three synergistic components: (1) Efficient MLLM Fine-Tuning for Complex Defect Inspection, (2) Lightweight Multiscale Scene-Complexity Estimation, and (3) Adaptive Edge-Cloud Scheduler. Together, these modules enable robust defect detection by tailoring multimodal reasoning to scene complexity and dynamically balancing computation between edge and cloud resources. Experimental results on MVTec AD and KSDD2 datasets demonstrate that SAEC attains 85.11% and 82.72% accuracy, surpassing Qwen by 22.1% and 20.8%, and LLaVA by 33.3% and 31.6%. It also reduces runtime by up to 22.4% and cuts energy per correct decision by 40%-74%. The code is available at https://github.com/YuHao-Tian/SAEC. |
| title | SAEC: Scene-Aware Enhanced Edge-Cloud Collaborative Industrial Vision Inspection with Multimodal LLM |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.17136 |