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

TL;DR

AMIEOD introduces an adaptive image enhancement framework with multiple strategies and detection-guided optimization to significantly improve object detection in low-light scenes.

Contribution

It proposes a novel multi-experts enhancement module, detection-guided loss, and expert selection mechanism for better low-illumination object detection.

Findings

Significant improvement in detection accuracy under low-light conditions.

Effective dynamic selection of enhancement strategies during inference.

Compatibility with existing detection algorithms enhances performance in dim scenes.

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 AMIEOD, an image enhancement-enabled object detection framework for low-illumination scenes, where the two tasks are jointly optimized in a detection performance-oriented manner. Specifically, to fully exploit the information in poorly lit images, a Multi-Experts Image Enhancement Module (MEIEM) is proposed, which leverages diverse enhancement strategies. On this basis, aiming to better align the MEIEM with the detection task, we propose a Detection-Guided Regression Loss (DGRL) that utilizes the detection result to decide the regression target. Moreover, to dynamically select the most suitable enhancement strategy from MEIEM during inference, we construct an Expert Selection Module (ESM) guided by the proposed Detection-Guided Cross-Entropy (DGCE) loss, which formulates the optimization of ESM as a classification task. The improved method is well-matched with current detection algorithms to improve their performance in dim scenes. Extensive experiments on multiple datasets demonstrate that the proposed method significantly improves object detection accuracy in low-illumination conditions. Our code has been released at https://github.com/scujayfantasy/AMIEOD