Low-Light Small Target Detection Method Combining Feature Fusion Enhancement and Detail Features

JIANG Ze-tao; CHENG Liu-ming; YANG Jian-chen

doi:10.12263/DZXB.20241155

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Low-Light Small Target Detection Method Combining Feature Fusion Enhancement and Detail Features

PAPERS | 更新时间：2025-12-10

- Low-Light Small Target Detection Method Combining Feature Fusion Enhancement and Detail Features
- ACTA ELECTRONICA SINICA Vol. 53, Issue 7, Pages: 2229-2240(2025)
- 作者机构：
  
  桂林电子科技大学广西图像图形处理与智能处理重点实验室，广西桂林 541004
- 作者简介：
- 基金信息：
  
  National Nature Science Foundation of China(62473105);Guangxi Key Laboratory of Image and Graphic Intelligent Processing(GIIP2302;GIIP2303;GIIP2304);Innovation Project of GUET Graduate Education(2024YCXB09;2024YCXS039;2024YCXS035)
- DOI：10.12263/DZXB.20241155
  CLC： TP391;
- Received：24 December 2024，
  
  Revised：2025-04-28，
  
  Published：25 July 2025
- 稿件说明：
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江泽涛, 程留明, 杨建琛. 结合特征融合增强和细节特征的低照度小目标检测方法[J]. 电子学报, 2025, 53(07): 2229-2240.

JIANG Ze-tao, CHENG Liu-ming, YANG Jian-chen. Low-Light Small Target Detection Method Combining Feature Fusion Enhancement and Detail Features[J]. Acta Electronica Sinica, 2025, 53(07): 2229-2240.
江泽涛, 程留明, 杨建琛. 结合特征融合增强和细节特征的低照度小目标检测方法[J]. 电子学报, 2025, 53(07): 2229-2240. DOI：10.12263/DZXB.20241155

JIANG Ze-tao, CHENG Liu-ming, YANG Jian-chen. Low-Light Small Target Detection Method Combining Feature Fusion Enhancement and Detail Features[J]. Acta Electronica Sinica, 2025, 53(07): 2229-2240. DOI：10.12263/DZXB.20241155

摘要

正常照度环境下的图像小目标检测具有挑战性，而低照度环境下的图像因低亮度、低对比度、低信噪比等，信息丢失严重，使得小目标特征信息更加弱化，更加难以获取特征等信息，从而导致低照度环境下小目标检测的研究极少.本文针对这一问题，提出一个低照度小目标检测方法MC-YOLO.该方法包含多尺度特征融合增强（Multi-Scale Feature Fusion Enhancement，MFFE）模块、细节特征提取（Detail Feature Extraction，DFE）模块、Neck模块和Head模块.该方法的第一部分由MFFE模块采用可变形卷积和多尺度特征融合来提取低照度环境下的小目标特征，再通过全局平均池化进行全局特征增强，从而使小目标特征信息更加显著；第二部分由DFE模块在充分利用上下文信息提取小目标特征的同时保留小目标的位置信息，解决低照度小目标物体细节特征信息易丢失的问题；第三部分由Neck模块进行特征提取和多尺度特征融合；最后，在Head模块的大分辨率特征图上引入小目标检测层，实现低照度小目标的检测.实验结果表明，本文方法在低照度小目标检测精度方面具有良好的表现，在自制低照度小目标数据集LLSOD上的mAP为83.2%，比目前先进的目标检测方法YOLOv11高出3.6%.

Abstract

The detection of small targets in normal illumination conditions is challenging. In low-light environments

images suffer from severe information loss due to low brightness

low contrast

and low signal-to-noise ratio

which further weakens the feature information of small targets

making feature extraction more difficult. As a result

research on small target detection in low-light environments is scarce. To address this issue

this paper proposes a low-light small target detection method

MC-YOLO. The MC-YOLO includes four modules: multi-scale feature fusion enhancement (MFFE) module

detail feature extraction (DFE) module

Neck module and Head module. Firstly

the method uses the MFFE module to extract small target features in low-light environment through deformable convolutions and multi-scale feature fusion and to enhance global features through global average pooling making small target feature information more salient. Next

the DFE module fully utilizes contextual information to extract small target features while preserving the positional information of the small targets

which solves the problem of easy loss of detail feature information of small targets in low-light environments. Then

the neck module performs feature extraction and multi-scale feature fusion. Finally

the head module introduces a small target detection layer on the high-resolution feature map to detect small targets in low-light environments. Experimental results show that this method performs well in the accuracy of low-light small target detection

with the mAP of 83.2% on the self-made low-light small target dataset LLSOD

which is 3.6% higher than the current advanced target detection method YOLOv11.

关键词

Keywords

references

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