Performance analysis of the YOLO Object Detection Algorithm in embedded systems: generated code vs. native implementation

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dc.contributor.authorMartínez Otero, Pablo
dc.contributor.authorTellaeche Iglesias, Alberto
dc.contributor.authorHernández Melero, Mar
dc.date.accessioned2026-04-29T18:19:53Z
dc.date.available2026-04-29T18:19:53Z
dc.date.issued2026-03-12
dc.date.updated2026-04-29T18:19:53Z
dc.description.abstractThis paper evaluates the current maturity of automatic code-generation workflows for deploying modern CNN-based object detectors on embedded GPU platforms. We compare a native pipeline against a code generation pipeline through a Model-Based Engineering (MBE) approach, using YOLOv8/YOLOv9 inference on NVIDIA Jetson Orin Nano and Jetson AGX Orin as representative edge-GPU workloads. We report detection-quality metrics (mAP, PR curves) and system-level metrics (latency distribution and initialization overhead) under a controlled single-class scenario based on a CARLA-generated sequence with frame-level annotations. Absolute accuracy and latency values are scenario-dependent and may vary under different camera optics, illumination, motion blur, sensor noise, occlusion patterns, and multi-class scene. Results quantify the performance gap between code generation and native pipelines and show that, for the evaluated workloads, the automated pipeline remains less competitive in both latency and accuracy. We discuss the implications of this gap for deployment workflows in safety-oriented domains, and we outline bottlenecks that should be addressed. The study is intended as a controlled traffic-light detection micro-benchmark and does not aim to validate full ADAS perception stacks.en
dc.identifier.citationMartínez Otero, P., Tellaeche, A., & Hernández Melero, M. (2026). Performance analysis of the YOLO Object Detection Algorithm in embedded systems: generated code vs. native implementation. Computation, 14(3). https://doi.org/10.3390/COMPUTATION14030067
dc.identifier.doi10.3390/COMPUTATION14030067
dc.identifier.eissn2079-3197
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5824
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2026 by the authors
dc.subject.otherCode generation
dc.subject.otherDeep learning
dc.subject.otherEmbedded systems
dc.subject.otherMicro-benchmark
dc.subject.otherTraffic light detection
dc.subject.otherYOLO
dc.titlePerformance analysis of the YOLO Object Detection Algorithm in embedded systems: generated code vs. native implementationen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue3
oaire.citation.titleComputation
oaire.citation.volume14
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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