A Low-Illumination Object Detection Method Based on Night-YOLOX

JIANG Ze-tao; SHI Dao-quan; LEI Xiao-chun; HE Yu-ting; LI Hui; ZHOU Yong-gang

doi:10.12263/DZXB.20221396

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A Low-Illumination Object Detection Method Based on Night-YOLOX

PAPERS | 更新时间：2025-12-08

- A Low-Illumination Object Detection Method Based on Night-YOLOX
- ACTA ELECTRONICA SINICA Vol. 51, Issue 10, Pages: 2821-2830(2023)
- 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(GIIP2203;GIIP2204);Innovation Project of Guangxi Graduate Education(YCSW2022269)
- DOI：10.12263/DZXB.20221396
  CLC： TP391
- Received：25 December 2022，
  
  Revised：2023-04-10，
  
  Published：25 October 2023
- 稿件说明：
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江泽涛,施道权,雷晓春等.一种基于Night-YOLOX的低照度目标检测方法[J].电子学报,2023,51(10):2821-2830.

JIANG Ze-tao,SHI Dao-quan,LEI Xiao-chun,et al.A Low-Illumination Object Detection Method Based on Night-YOLOX[J].ACTA ELECTRONICA SINICA,2023,51(10):2821-2830.
江泽涛,施道权,雷晓春等.一种基于Night-YOLOX的低照度目标检测方法[J].电子学报,2023,51(10):2821-2830. DOI： 10.12263/DZXB.20221396.

JIANG Ze-tao,SHI Dao-quan,LEI Xiao-chun,et al.A Low-Illumination Object Detection Method Based on Night-YOLOX[J].ACTA ELECTRONICA SINICA,2023,51(10):2821-2830. DOI： 10.12263/DZXB.20221396.

摘要

由于在低照度场景下获取的图像具有亮度弱、对比度低、噪声多和细节丢失等特点，使用现有的检测模型对低照度图像进行目标检测会出现定位不准确和分类错误，从而导致最终的检测精度偏低.针对以上现象，本文提出了一种基于Night-YOLOX的低照度目标检测方法.该方法首先设计了一个低级特征聚集模块（Low-level Feature Gathering Module，LFGM）与主干网络合并.在低照度场景下捕获更多有效的低级特征有利于定位目标，该模块通过聚集浅层特征图中具有判别性的低级特征并送入高级特征图和深层卷积阶段中，以补偿在对低照度图像进行特征提取过程中边缘、轮廓和纹理等低级特征的缺失.然后，设计了一种注意力引导块（Attention Guidance Block，AGB）嵌入检测模型的颈部结构，从而减少低照度图像中噪声干扰的影响，引导检测模型推断出特征图中完整的对象区域范围并提取更多有用的对象特征信息，以提高目标分类的准确性.最后，在真实低照度图像数据集ExDark上进行实验，结果表明所提出的Night-YOLOX相比于其它主流的目标检测方法，在低照度场景下具有更好的检测性能.

Abstract

Images captured in low-illumination environments often have many quality problems

such as weak brightness

low contrast

much noise

and detail loss. These problems will lead to inaccurate localization and object classification errors when using the existing object detection models to detect low-light images

resulting in low detection accuracy. Aiming at the above phenomena

this paper proposes a low-illumination object detection method called Night-YOLOX. First

the low-level feature gathering module (LFGM) is designed to be incorporated into the backbone. Capturing more effective low-level features in low-illumination scenes is beneficial to locating objects. The LFGM aggregates more discriminative low-level features from the shallow feature maps and feeds them into the high-level feature maps and the deep convolution stages

so as to compensate for the loss of low-level edge

contour

and texture features during feature extraction in low-light images. Then

the attention guidance block (AGB) is designed to be embedded in the neck of the detection model. The AGB reduces the influence of noise interference in low-light images

guides the detection model to infer the complete object regions and extract more useful object feature information

so as to improve the accuracy of object classification. Finally

experiments are conducted on the real low-light image dataset ExDark. The experimental results show that compared with other mainstream object detection methods

the proposed Night-YOLOX has better detection performance in low-illumination scenarios.

关键词

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references

HUANG Y , JIANG Z , LAN R , et al . Infrared image super-resolution via transfer learning and PSRGAN [J]. IEEE Signal Processing Letters , 2021 , 28 : 982 - 986 .

ZHANG H , CHANG H , MA B , et al . Dynamic R-CNN: Towards high quality object detection via dynamic training [C]// European Conference on Computer Vision . Berlin : Springer , 2020 : 260 - 275 .

SUN P , ZHANG R , JIANG Y , et al . Sparse R-CNN: End-to-end object detection with learnable proposals [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 14454 - 14463 .

QIAO S , CHEN L C , YUILLE A . Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 10213 - 10224 .

BOCHKOVSKIY A , WANG C Y , LIAO H Y M . YOLOV4: Optimal speed and accuracy of object detection [EB/OL]. ( 2020-04-23 )[ 2022-12-05 ]. https://arxiv.org/abs/2004.10934 https://arxiv.org/abs/2004.10934 .

GE Z , LIU S , WANG F , et al . YOLOX: Exceeding YOLO series in 2021 [EB/OL]. ( 2021-07-28 )[ 2022-12-05 ]. https://arxiv.org/abs/2107.08430 https://arxiv.org/abs/2107.08430 .

WANG W , YANG W , LIU J . Hla-face: Joint high-low adaptation for low light face detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 16195 - 16204 .

LOH Y P , CHAN C S . Getting to know low-light images with the exclusively dark dataset [J]. Computer Vision and Image Understanding , 2019 , 178 : 30 - 42 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [J]. Attention is all you need[EB/OL] . ( 2017-06-12 )[ 2022-12-05 ]. https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762 .

ZHANG H , GOODFELLOW I , METAXAS D , et al . Self-attention generative adversarial networks [C]// International Conference on Machine Learning . New York : PMLR , 2019 : 7354 - 7363 .

GUO M H , XU T X , LIU J J , et al . Attention mechanisms in computer vision: A survey [J]. Computational Visual Media , 2022 , 8 : 331 - 368 .

LIU S , QI L , QIN H , et al . Path aggregation network for instance segmentation [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 8759 - 8768 .

LOSHCHILOV I , HUTTER F . Sgdr: Stochastic gradient descent with warm restarts [EB/OL]. ( 2016-08-13 )[ 2022-12-05 ]. https://arxiv.org/abs/1608.03983v3 https://arxiv.org/abs/1608.03983v3 .

DENG J , DONG W , SOCHER R , et al . Imagenet: A large-scale hierarchical image database [C]// 2009 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2009 : 248 - 255 .

HU J , SHEN L , SUN G . Squeeze-and-excitation networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 7132 - 7141 .

PARK J , WOO S , LEE J Y , et al . Bam: Bottleneck attention module [EB/OL]. ( 2018-07-17 )[ 2022-12-05 ]. https://arxiv.org/abs/1807.06514v1 https://arxiv.org/abs/1807.06514v1 .

HOU Q , ZHOU D , FENG J . Coordinate attention for efficient mobile network design [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 13713 - 13722 .

LI X , WANG W , HU X , et al . Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 11632 - 11641 .

TAN M , PANG R , LE Q V . Efficientdet: Scalable and efficient object detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2020 : 10781 - 10790 .

WANG J , ZHANG W , CAO Y , et al . Side-aware boundary localization for more precise object detection [C]// European Conference on Computer Vision . Berlin : Springer , 2020 : 403 - 419 .

ZHANG H , WANG Y , DAYOUB F , et al . Varifocalnet: An iou-aware dense object detector [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 8514 - 8523 .

CHEN Q , WANG Y , YANG T , et al . You only look one-level feature [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 13039 - 13048 .

TIAN Z , SHEN C , CHEN H , et al . Fcos: A simple and strong anchor-free object detector [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 4 ): 1922 - 1933 .

ZHANG X , WAN F , LIU C , et al . Learning to match anchors for visual object detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 6 ): 3096 - 3109 .

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Related Author

JIANG Ze-tao

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Related Institution

Guangxi Key Laboratory of Image and Graphic Intelligent Processing， Guilin University of Electronic Technology

The Key Laboratory of Image and Graphic Intelligent Processing in Guangxi，Guilin University of Electronic Technology

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Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation, Hunan University of Technology and Business

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