自适应引导语义增强：基于多模态大语言模型的边缘-云协同目标检测
Adaptive Guidance Semantically Enhanced via Multimodal LLM for Edge-Cloud Object Detection
AI总结：
针对复杂场景下目标检测性能退化问题，提出基于多模态大语言模型的自适应语义增强边缘-云协同方法，在保持精度下延迟降79%、计算成本降70%。
摘要：
传统目标检测方法在低光照和严重遮挡等复杂场景中，由于缺乏高层语义理解，面临性能下降的挑战。为解决这一问题，本文提出了一种基于多模态大语言模型（MLLM）的自适应引导语义增强边缘-云协同目标检测方法，在准确性和效率之间实现了有效平衡。具体而言，该方法首先采用指令微调使MLLM能够生成结构化的场景描述；随后设计了一种自适应映射机制，将语义信息动态转换为边缘检测器的参数调整信号，实现实时语义增强。在边缘-云协同推理框架内，系统根据置信度分数自动选择调用云端语义引导或直接输出边缘检测结果。实验表明，所提方法在复杂场景中有效提升了检测准确性和效率。具体而言，在低光照和高遮挡场景下，该方法可在保持准确性的同时，将延迟降低超过79%，计算成本降低70%。
英文摘要：
Traditional object detection methods face performance degradation challenges in complex scenarios such as low-light conditions and heavy occlusions due to a lack of high-level semantic understanding. To address this, this paper proposes an adaptive guidance-based semantic enhancement edge-cloud collaborative object detection method leveraging Multimodal Large Language Models (MLLM), achieving an effective balance between accuracy and efficiency. Specifically, the method first employs instruction fine-tuning to enable the MLLM to generate structured scene descriptions. It then designs an adaptive mapping mechanism that dynamically converts semantic information into parameter adjustment signals for edge detectors, achieving real-time semantic enhancement. Within an edge-cloud collaborative inference framework, the system automatically selects between invoking cloud-based semantic guidance or directly outputting edge detection results based on confidence scores. Experiments demonstrate that the proposed method effectively enhances detection accuracy and efficiency in complex scenes. Specifically, it can reduce latency by over 79% and computational cost by 70% in low-light and highly occluded scenes while maintaining accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
