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基于改进YOLOv8的低光行人检测算法.

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Title: 基于改进YOLOv8的低光行人检测算法.
Alternate Title: An improved low-light pedestrian detection algorithm based on YOLOv8.
Authors: 徐广平1 2788596417@qq.com, 徐慧英1, 朱信忠1, 黄 晓2, 王舒梦1, 宋 杰1
Source: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Mar2026, Vol. 48 Issue 3, p540-550. 11p.
Subjects: Image enhancement (Imaging systems), Object recognition (Computer vision), Real-time computing, Algorithms
Abstract (English): In order to solve the problem that the current mainstream low-light pedestrian detection framework has poor performance due to insufficient image brightness and contrast in this task, this paper proposes the RetinaHA-YOLOv8 algorithm. The algorithm uses RetinexFormer as a pre-processing module to restore the damaged image, ensuring that the subsequent algorithm can extract clearer and more useful features from the enhanced image. Additionally, it uses the hybrid attention transformation (HAT) attention mechanism to retain key information in the initial stage and promote deep fusion after feature fusion. Finally, in order to balance the additional computational burden and meet the real-time detection requirements, the online re-parameterized convolution technology is introduced to improve the inference speed and frames per second while maintaining the detection accuracy. The experimental results verify the effectiveness of the RetinaHA-YOLOv8 algorithm on the public low-light pedestrian detection dataset, with AP increased by 5.4%, 11.7% and 9.5% respectively, while meeting the realtime requirements in practical applications. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 针对目前主流低光行人检测框架因为此任务中的图像亮度和对比度不足的原因导致性能下 降的问题,提出了RetinaHA-YOLOv8算法。该算法通过采用RetinexFormer作为前置处理模块来恢复 受损图像,确保后续算法能够从增强后的图像中提取到更加清晰和有用的特征;并利用HAT注意力机制 在初始阶段保留关键信息并在特征融合后促进深度融合;最后为平衡额外计算负担并满足实时检测需求, 引入在线重参数化卷积技术,以提高推理速度和每秒处理的帧数,同时保持检测精度。实验结果验证了 RetinaHA-YOLOv8算法在公开低光行人检测数据集上的有效性,AP 分别提升5.4%,11.7%和9.5%, 且满足实际应用的实时性要求. [ABSTRACT FROM AUTHOR]
Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: 基于改进YOLOv8的低光行人检测算法.
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  Data: An improved low-light pedestrian detection algorithm based on YOLOv8.
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  Data: <searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: In order to solve the problem that the current mainstream low-light pedestrian detection framework has poor performance due to insufficient image brightness and contrast in this task, this paper proposes the RetinaHA-YOLOv8 algorithm. The algorithm uses RetinexFormer as a pre-processing module to restore the damaged image, ensuring that the subsequent algorithm can extract clearer and more useful features from the enhanced image. Additionally, it uses the hybrid attention transformation (HAT) attention mechanism to retain key information in the initial stage and promote deep fusion after feature fusion. Finally, in order to balance the additional computational burden and meet the real-time detection requirements, the online re-parameterized convolution technology is introduced to improve the inference speed and frames per second while maintaining the detection accuracy. The experimental results verify the effectiveness of the RetinaHA-YOLOv8 algorithm on the public low-light pedestrian detection dataset, with AP increased by 5.4%, 11.7% and 9.5% respectively, while meeting the realtime requirements in practical applications. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 针对目前主流低光行人检测框架因为此任务中的图像亮度和对比度不足的原因导致性能下 降的问题,提出了RetinaHA-YOLOv8算法。该算法通过采用RetinexFormer作为前置处理模块来恢复 受损图像,确保后续算法能够从增强后的图像中提取到更加清晰和有用的特征;并利用HAT注意力机制 在初始阶段保留关键信息并在特征融合后促进深度融合;最后为平衡额外计算负担并满足实时检测需求, 引入在线重参数化卷积技术,以提高推理速度和每秒处理的帧数,同时保持检测精度。实验结果验证了 RetinaHA-YOLOv8算法在公开低光行人检测数据集上的有效性,AP 分别提升5.4%,11.7%和9.5%, 且满足实际应用的实时性要求. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3969/j.issn.1007-130X.2026.03.016
    Languages:
      – Code: chi
        Text: Chinese
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 540
    Subjects:
      – SubjectFull: Image enhancement (Imaging systems)
        Type: general
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Real-time computing
        Type: general
      – SubjectFull: Algorithms
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    Titles:
      – TitleFull: 基于改进YOLOv8的低光行人检测算法.
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            NameFull: 徐广平
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            NameFull: 朱信忠
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              M: 03
              Text: Mar2026
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              Y: 2026
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