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Journal Article
MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing
- Authors
- Jihyun Seo, Sumin Jang, Jaegeun Cha, Hyunhwa Choi, Daewon Kim, Sunwook Kim
- Issue Date
- 2023-05
- Citation
- Sensors, v.23, no.10, pp.1-16
- ISSN
- 1424-8220
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Language
- English
- Type
- Journal Article
- Abstract
- The demand for deep learning frameworks capable of running in edge computing environments is rapidly increasing due to the exponential growth of data volume and the need for real-time processing. However, edge computing environments often have limited resources, necessitating the distribution of deep learning models. Distributing deep learning models can be challenging as it requires specifying the resource type for each process and ensuring that the models are lightweight without performance degradation. To address this issue, we propose the Microservice Deep-learning Edge Detection (MDED) framework, designed for easy deployment and distributed processing in edge computing environments. The MDED framework leverages Docker-based containers and Kubernetes orchestration to obtain a pedestrian-detection deep learning model with a speed of up to 19 FPS, satisfying the semi-real-time condition. The framework employs an ensemble of high-level feature-specific networks (HFN) and low-level feature-specific networks (LFN) trained on the MOT17Det dataset, achieving an accuracy improvement of up to AP50 and AP0.18 on MOT20Det data.
- KSP Keywords
- Data Volume, Deep learning framework, Edge Computing, Limited resources, Low-level feature, Real-Time processing, Real-time conditions, accuracy improvement, deep learning(DL), deep learning models, distributed processing
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
