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arXiv Computer Vision By Zhouzhi Xiong, Zimo Zeng, Yi Chen, Shuqi Xu, Yunfeng Yan, Donglian Qi

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

Read the original on arXiv Computer Vision →

DenseScout is a 1.01M‑parameter dense‑response selector designed for edge platforms that directly optimizes ranked patch‑center prioritization, eliminating the need for detector‑style box regression. It aligns output representation, supervision, and decoding, and is jointly designed with transport‑aware execution and QoS‑oriented evaluation. Experiments on VisDrone and DOTA show that DenseScout achieves stronger low‑budget recall than detector‑derived selectors, and cross‑platform profiling on Jetson Orin NX and RK3588 demonstrates that deployable utility depends on selector quality, memory movement, and heterogeneous runtime realization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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