Low Illumination Image Object Detection Method Based on ICFIE-YOLO

QIN Jia-qi; JIANG Ze-tao; LEI Xiao-chun

doi:10.12263/DZXB.20240648

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Low Illumination Image Object Detection Method Based on ICFIE-YOLO

PAPERS | 更新时间：2025-12-08

- Low Illumination Image Object Detection Method Based on ICFIE-YOLO
- ACTA ELECTRONICA SINICA Vol. 53, Issue 2, Pages: 514-526(2025)
- 作者机构：
  
  桂林电子科技大学广西图像图形与智能处理重点实验室，广西桂林 541004
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62172118);Key Projects of Guangxi Natural Science Foundation(2021GXNSFDA196002);Guangxi Key Laboratory of Image and Graphic Intelligent Processing(GIIP2302;GIIP2303;GIIP2304);Innovation Project of Guangxi Graduate Education(2024YCXB09;2024YCXS039)
- DOI：10.12263/DZXB.20240648
  CLC： TP391;
- Received：09 July 2024，
  
  Revised：2024-12-16，
  
  Published：25 February 2025
- 稿件说明：
移动端阅览
秦嘉奇, 江泽涛, 雷晓春. 基于ICFIE-YOLO的低照度图像目标检测方法[J]. 电子学报, 2025, 53(02): 514-526.

QIN Jia-qi, JIANG Ze-tao, LEI Xiao-chun. Low Illumination Image Object Detection Method Based on ICFIE-YOLO[J]. Acta Electronica Sinica, 2025, 53(02): 514-526.
秦嘉奇, 江泽涛, 雷晓春. 基于ICFIE-YOLO的低照度图像目标检测方法[J]. 电子学报, 2025, 53(02): 514-526. DOI：10.12263/DZXB.20240648

QIN Jia-qi, JIANG Ze-tao, LEI Xiao-chun. Low Illumination Image Object Detection Method Based on ICFIE-YOLO[J]. Acta Electronica Sinica, 2025, 53(02): 514-526. DOI：10.12263/DZXB.20240648

摘要

低照度环境下获取的图像往往亮度低、对比度低、光照不均匀，从而造成图像特征变弱及模糊难于提取，同时在有限提取的特征中也存在大量噪声信息，导致目标难于检测识别，因而现有低照度目标检测成果极少.针对低照度目标特征难于提取及特征空间噪声大的问题，本文提出一种基于光照矫正与特征交互增强（Illumination Correction and Feature Interacted Enhancement，ICFIE-YOLO）网络的低照度目标检测方法.该方法首先利用提出的ICFIE-YOLO内部多尺度光照矫正网络（Multi Scale Illumination Correction Network，MSICN）对低照度图像进行光照矫正，突出隐藏在图像背景中目标的模糊特征，使特征提取模块能更好地提取到目标特征；其次，为充分利用有效特征信息，过滤特征图中的噪声干扰，提出特征交互增强（Feature Interacted Enhancement，FIE）检测头，通过特征注意力交互方式实现特征增强，建立低照度图像中各个区域特征之间的空间关联和语义关联，从而抑制噪声对有效特征的干扰，实现降噪效果；最后，在增强特征及去除噪声的基础上用改进的检测头实现高精度目标检测.在ExDark和DarkFace数据集上的实验表明，所提方法较主流目标检测方法mAP提高2.1个百分点以上，较现有低照度目标检测方法召回率提高4.2个百分点以上，同时召回率较基线模型提高了2.6个百分点，所提方法具有较好的泛化性.

Abstract

Images obtained in low light environments often have low brightness

low contrast

and uneven lighting

resulting in weakened and blurred image features that are difficult to extract. At the same time

there is also a large amount of noise information in the limited extracted features

making it difficult to detect and recognize objects. Therefore

there are very few existing low light object detection results. This paper proposes a low illumination object detection method based on the Illumination Correction and Feature Interaction Enhancement (ICFIE-YOLO) network to address the difficulties in extracting features from low illumination objects and the large noise in the feature space. This method first utilizes the proposed ICFIE-YOLO internal Multi Scale Illumination Correction Network (MSICN) to correct low illumination images

highlighting the blurry features of objects hidden in the image’s background

and enabling the feature extraction module to better extract object features. Secondly

to fully utilize effective feature information and filter out noise interference in feature maps

a Feature Interacted Enhancement (FIE) detection head is proposed. Through feature attention interaction

feature enhancement is achieved

establishing spatial and semantic correlations between features in different regions of low illumination images

thereby suppressing the interference of noise on effective features and achieving feature enhancement. Finally

on the basis of enhancing features and removing noise

an improved detection head is used to achieve high-precision object detection. Experiments on the ExDark and DarkFace datasets show that the proposed Method improves mAP by over 2.1 percentage points compared to mainstream object detection models

increases recall by over 4.2 percentage points compared to existing low light object detection Methods

and improves recall by 2.6 percentage points compared to baseline models. The proposed Method has good generalization performance.

关键词

Keywords

references

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