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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01528 |
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| _version_ | 1866917305559023616 |
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| author | Zhang, Yutian Pei, Zhongyi Mao, Yi Wang, Chen Liu, Lin Wang, Jianmin |
| author_facet | Zhang, Yutian Pei, Zhongyi Mao, Yi Wang, Chen Liu, Lin Wang, Jianmin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01528 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Boosting AI Reliability with an FSM-Driven Streaming Inference Pipeline: An Industrial Case Zhang, Yutian Pei, Zhongyi Mao, Yi Wang, Chen Liu, Lin Wang, Jianmin Computer Vision and Pattern Recognition 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. |
| title | Boosting AI Reliability with an FSM-Driven Streaming Inference Pipeline: An Industrial Case |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.01528 |