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English(EN) Real-Time Source-Free Object Detection

新的SFOD方法推进了速度-准确性-大小的权衡 · 跟踪了2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的实时无源目标检测（SFOD）方法，该方法在提高准确性的同时降低了计算要求。基于YOLOv10架构，提出的双头伪标签融合（DHF）和多尺度自适应表示多样化（MARD）技术增强了领域偏移数据的适应过程。与现有的SFOD方法相比，该方法在平均精度均值（mAP）和吞吐量方面取得了显著的提高，并且参数更少。 AI

影响 这项研究可能导致更高效、更准确的自动驾驶汽车和机器人目标检测系统。

排序理由 该集群包含一篇详细介绍新目标检测方法的论文。

在 arXiv cs.AI 阅读 →

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新的SFOD方法推进了速度-准确性-大小的权衡 · 跟踪了2个来源

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该集群包含一篇详细介绍新目标检测方法的论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sairam VCR, Varun Gopal, Poornima Jain, Vineeth N Balasubramanian, Muhammad Haris Khan ·

    实时无源目标检测

    arXiv:2606.31834v1 Announce Type: cross Abstract: Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectu…

  2. arXiv cs.CV TIER_1 English(EN) · Muhammad Haris Khan ·

    实时无源目标检测

    Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone. We show this t…

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