Publicação
Distributed Computing for Real‐Time Computer Vision in Manufacturing
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| dc.contributor.advisor | Frazão, Luís Alexandre Lopes | |
| dc.contributor.author | Vieira, João David Moniz | |
| dc.date.accessioned | 2026-09-22T15:46:45Z | |
| dc.date.available | 2026-09-22T15:46:45Z | |
| dc.date.issued | 2026-07-14 | |
| dc.description.abstract | Modern manufacturing increasingly depends on distributed, software-based systems in which computer vision and inference workloads must operate under strict latency, availability and resource constraints. In such environments, end-to-end responsiveness is determined not only by model inference, but also by the supporting messaging, storage and orchestration layers, whose design becomes a cross-cutting concern for both system architects and Development and Operations (DevOps) practitioners. This work was carried out during an internship at Twevo S.A., a Portuguese company that develops AI-powered edge computer vision solutions for the manufacturing sector, and is framed within the evolution of the company’s VIEXPAND AI platform. The objective was to study, design a distributed edge architecture capable of keeping end-to-end latency low while improving availability and lowering the cost of developing new components, while leveraging industry-standard DevOps workflows to ensure long-term maintainability. Four workstreams were implemented. A lightweight storage subsystem based on SQLite was designed to replace a legacy text log based persistence mechanism, with tuned journalling modes and a refactored worker-thread concurrency model. A containerisation strategy for the graphical components of the platform was developed, combining Docker images with a remote-desktop-based display layer to enable reproducible, isolated and securely exposed deployments. A Kafka-based messaging backbone was introduced to replace a point-to-point Transmission Control Protocol (TCP) messaging system, including topic and partition layouts, producer and consumer tuning for low latency, and a thin librdkafka wrapper to support deterministic benchmarking and integration. Finally, a deterministic perception-to-action controller was implemented to translate vendor-agnostic orders into machine-executable instructions. The artefacts were assessed through targeted validation activities, including database latency and resource measurements, Kafka broker and integration tests, a container display-latency comparison, and actuator functional and latency tests. The remaining contribution consists of a set of integrated, production-oriented components and a body of design principles and best practices for deploying low-latency, resourceefficient edge inference systems in manufacturing environments. | eng |
| dc.identifier.tid | 204364523 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/16895 | |
| dc.language.iso | eng | |
| dc.rights.uri | N/A | |
| dc.subject | Edge Computing | |
| dc.subject | Low Latency | |
| dc.subject | Stream Processing | |
| dc.subject | Real-Time Data Processing | |
| dc.subject | Kafka | |
| dc.subject | DevOps | |
| dc.subject | Computer Vision | |
| dc.title | Distributed Computing for Real‐Time Computer Vision in Manufacturing | |
| dc.type | master thesis | |
| dspace.entity.type | Publication |