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English(EN) AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes

AMIEOD框架增强低光照场景下的目标检测

研究人员开发了AMIEOD,一个旨在增强低照度环境下目标检测能力的新型框架。该系统联合优化图像增强和目标检测任务,利用一个采用多种增强策略的多专家图像增强模块(MEIEM)。该框架还引入了一个检测引导回归损失(DGRL)和一个专家选择模块(ESM),以根据检测性能动态调整增强技术。 AI

影响 提高在具有挑战性的低光照条件下的目标检测准确性,可能使自主系统和监控受益。

排序理由 这是一篇详细介绍图像处理和目标检测新技术框架的研究论文。

在 arXiv cs.CV 阅读 →

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AMIEOD框架增强低光照场景下的目标检测

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xiaochen Huang, Honggang Chen, Weicheng Zhang, Xiaobo Dai, Yongyi Li, Linbo Qing, Xiaohai He ·

    AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes

    arXiv:2605.06084v1 Announce Type: new Abstract: In multimedia application scenarios, images captured under low-illumination conditions often lead to lower accuracy in visual perception tasks compared to those taken in well-lit environments. To tackle this challenge, we propose AM…

  2. arXiv cs.CV TIER_1 English(EN) · Xiaohai He ·

    AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes

    In multimedia application scenarios, images captured under low-illumination conditions often lead to lower accuracy in visual perception tasks compared to those taken in well-lit environments. To tackle this challenge, we propose AMIEOD, an image enhancement-enabled object detect…