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DenseScout optimizes tiny object selection for edge platforms via algorithm-system co-design

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 3 sources

Researchers have developed DenseScout, a novel algorithm-system co-design approach for efficient tiny object selection on edge devices. This lightweight selector, with only 1.01 million parameters, prioritizes candidate image patches more effectively than traditional detector-based methods under strict compute and latency constraints. The system also incorporates a transport-aware runtime realization and a QoS-constrained recall metric to ensure performance within deadlines on heterogeneous hardware. AI

Summary written by gemini-2.5-flash-lite from 3 sources. How we write summaries →

IMPACT Optimizes tiny object perception on resource-constrained edge devices by integrating algorithm and system design.

RANK_REASON This is a research paper detailing a new algorithm and system co-design for object selection on edge platforms.

Read on arXiv cs.CV →

COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 ·

    DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platforms

    Deploying tiny object perception on edge platforms is challenging because practical systems must satisfy both strict compute budgets and end-to-end latency constraints. A common strategy is to first select a small number of candidate patches from a high-resolution image and then …

  2. arXiv cs.CV TIER_1 · Xiong Zhouzhi, Zimo Zeng, Yi Chen, Shuqi Xu, Yunfeng Yan, Donglian Qi ·

    DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platforms

    arXiv:2604.25300v1 Announce Type: new Abstract: Deploying tiny object perception on edge platforms is challenging because practical systems must satisfy both strict compute budgets and end-to-end latency constraints. A common strategy is to first select a small number of candidat…

  3. arXiv cs.CV TIER_1 · Donglian Qi ·

    DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platforms

    Deploying tiny object perception on edge platforms is challenging because practical systems must satisfy both strict compute budgets and end-to-end latency constraints. A common strategy is to first select a small number of candidate patches from a high-resolution image and then …

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