Formalized Modeling and Deterministic Traceability in Computer Vision Pipelines - A Case Study

This paper presents a formalized approach to modeling computer vision pipelines designed for non-intelligent, pre-installed camera networks. Using the Heimdall system as a case study, the paper proposes a deterministic framework that ensures traceable performance optimization and environment-specific adaptation for neural object and license plate recognition. The pipeline includes camera input, VPN-based data transfer, an edge image processing module, and a data reception software layer. The proposed formalization provides mathematical representation of parameter groups, such as resolution, confidence thresholds, and OCR preprocessing settings, and defines stable operational ranges to avoid performance collapse. Experimental validation is performed in an edge AI environment using real industrial camera feeds. Results indicate that deterministic parameter control and localized fine-tuning can significantly increase robustness without new hardware installation.