Title: ALow-Cost Real-Time Framework for Industrial Action Recognition Using Foundation Models Show Chinese title

Title: 一种基于基础模型的工业动作识别低成本实时框架

Abstract: Action recognition (AR) in industrial environments -- particularly for identifying actions and operational gestures -- faces persistent challenges due to high deployment costs, poor cross-scenario generalization, and limited real-time performance. To address these issues, we propose a low-cost real-time framework for industrial action recognition using foundation models, denoted as LRIAR, to enhance recognition accuracy and transferability while minimizing human annotation and computational overhead. The proposed framework constructs an automatically labeled dataset by coupling Grounding DINO with the pretrained BLIP-2 image encoder, enabling efficient and scalable action labeling. Leveraging the constructed dataset, we train YOLOv5 for real-time action detection, and a Vision Transformer (ViT) classifier is deceloped via LoRA-based fine-tuning for action classification. Extensive experiments conducted in real-world industrial settings validate the effectiveness of LRIAR, demonstrating consistent improvements over state-of-the-art methods in recognition accuracy, scenario generalization, and deployment efficiency. Show Chinese abstract

Abstract: 工业环境中的动作识别（AR）——特别是对于识别动作和操作手势——由于部署成本高、跨场景泛化能力差和实时性能有限而面临持续的挑战。 为解决这些问题，我们提出了一种基于基础模型的低成本实时工业动作识别框架，称为LRIAR，以在最小化人工标注和计算开销的同时提高识别准确性和可迁移性。 该框架通过将Grounding DINO与预训练的BLIP-2图像编码器结合，构建一个自动标记的数据集，从而实现高效且可扩展的动作标记。 利用构建的数据集，我们训练YOLOv5进行实时动作检测，并通过基于LoRA的微调开发了一个视觉Transformer（ViT）分类器用于动作分类。 在真实工业环境中进行的大量实验验证了LRIAR的有效性，在识别准确性、场景泛化能力和部署效率方面均表现出对最先进方法的一致改进。