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AMIEOD framework enhances object detection in low-light scenes

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed AMIEOD, a novel framework designed to enhance object detection in low-illumination environments. This system jointly optimizes image enhancement and object detection tasks, leveraging a Multi-Experts Image Enhancement Module (MEIEM) that employs various enhancement strategies. The framework also introduces a Detection-Guided Regression Loss (DGRL) and an Expert Selection Module (ESM) to dynamically adapt enhancement techniques based on detection performance. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Improves object detection accuracy in challenging low-light conditions, potentially benefiting autonomous systems and surveillance.

RANK_REASON This is a research paper detailing a new technical framework for image processing and object detection.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · 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 · 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…

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