Publicação
Distributed Computing for Real‐Time Computer Vision in Manufacturing
| Resumo: | 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. |
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| Autores principais: | Vieira, João David Moniz |
| Assunto: | Edge Computing Low Latency Stream Processing Real-Time Data Processing Kafka DevOps Computer Vision |
| Ano: | 2026 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Leiria e Oeste |
| Idioma: | inglês |
| Origem: | IC-online |
| Resumo: | 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. |
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Atividades financiadas
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