用状态机提升工业AI在未知场景下的推理可靠性
Boosting AI Reliability with an FSM-Driven Streaming Inference Pipeline: An Industrial Case
- 用有限状态机注入操作流程知识，动态修正视频流中的检测结果
- 在7000+图像、300+工况数据上，性能优于人工规则方案
- 适合工业视觉系统开发者，尤其关注模型鲁棒性的场景
AI在工业应用中常因训练数据外场景导致预测偏差和脆弱性。本文提出一种新型流式推理管道，通过显式引入先验知识增强数据驱动模型。研究聚焦于从监控视频自动统计挖机作业量的工业应用。方法将目标检测模型与有限状态机（FSM）结合，用操作流程知识指导并校正流式数据的AI预测。在包含12个工地视频、超过7,000张图像及300余个挖机作业工况的真实数据集上，该方法表现更优且更具鲁棒性，显著优于原始基于人工规则的方案。代码将于https://github.com/thulab/video-streamling-inference-pipeline公开。
原文摘要 · Abstract (English)
The widespread adoption of AI in industry is often hampered by its limited robustness when faced with scenarios absent from training data, leading to prediction bias and vulnerabilities. To address this, we propose a novel streaming inference pipeline that enhances data-driven models by explicitly incorporating prior knowledge. This paper presents the work on an industrial AI application that automatically counts excavator workloads from surveillance videos. Our approach integrates an object detection model with a Finite State Machine (FSM), which encodes knowledge of operational scenarios to guide and correct the AI's predictions on streaming data. In experiments on a real-world dataset of over 7,000 images from 12 site videos, encompassing more than 300 excavator workloads, our method demonstrates superior performance and greater robustness compared to the original solution based on manual heuristic rules. We will release the code at https://github.com/thulab/video-streamling-inference-pipeline.
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