Infrared image human vehicle detection algorithm based on SDL-YOLO

HUANG Guochao; CHANG Cheng; FANG Hongsu; YANG Wei

doi:10.11959/j.issn.1000-0801.2025176

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Infrared image human vehicle detection algorithm based on SDL-YOLO

Research and Development | 更新时间：2026-01-20

- Infrared image human vehicle detection algorithm based on SDL-YOLO
- Telecommunications Science Vol. 41, Issue 11, Pages: 175-188(2025)
- 作者机构：
  
  1.浙江交通职业技术学院汽车学院，浙江杭州 311112
  2.长安大学汽车学院，陕西西安 710064
- 作者简介：
- 基金信息：
  
  Shaanxi Natural Science Foundation Youth Project(2017JQ6045)
- DOI：10.11959/j.issn.1000-0801.2025176
  CLC： TP391
- Received：20 February 2025，
  
  Revised：2025-05-18，
  
  Published：20 November 2025
- 稿件说明：
移动端阅览
黄国潮,常城,方虹苏等.基于SDL-YOLO的红外图像人车检测算法[J].电信科学,2025,41(11):175-188.

HUANG Guochao,CHANG Cheng,FANG Hongsu,et al.Infrared image human vehicle detection algorithm based on SDL-YOLO[J].Telecommunications Science,2025,41(11):175-188.
黄国潮,常城,方虹苏等.基于SDL-YOLO的红外图像人车检测算法[J].电信科学,2025,41(11):175-188. DOI： 10.11959/j.issn.1000-0801.2025176.

HUANG Guochao,CHANG Cheng,FANG Hongsu,et al.Infrared image human vehicle detection algorithm based on SDL-YOLO[J].Telecommunications Science,2025,41(11):175-188. DOI： 10.11959/j.issn.1000-0801.2025176.

摘要

为了解决交通安全系统中红外道路行人及车辆目标检测中的漏检、误检及精度较差的问题，对基础YOLOv8进行改进，提出了改进SDL-YOLO算法。首先，通过引入空间深度转换卷积（SPD-Conv）以增强多尺度特征捕获，解决图像分辨率较低及目标检测困难问题；其后，引入一种轻量级上采样算子DySample，通过动态采样策略以优化复杂场景特征图，改善目标边界模糊及环境遮挡问题；最后，将检测头改为轻量化共享卷积检测头（lightweight shared convolutional detection，LSCD），以减少模型参数及计算量，在提升检测精度的同时保证轻量化。与此同时，在公开红外夜景数据集上使用量化评价指标精确率

、召回率

、平均精度mAP及每秒浮点操作数（floating-point operations per second，FLOPS）对算法进行综合评估。实验结果表明，SDL-YOLO相较原始算法，其精确率

提高1.5%、召回率

提高1.5%、平均精度mAP 50和mAP 50-95分别提高1.5%和1.7%，同时具有较低的模型计算复杂度FLOPS。通过检测效果可视化分析进一步论证了SDL-YOLO算法在提

升检测精度、改善漏检及误检方面的有效性。

Abstract

To solve the problems of missed detection

false detection

and poor accuracy in infrared road pedestrian and vehicle target detection in traffic safety systems

the basic YOLOv8 was improved and an improved SDL-YOLO algorithm was proposed. Firstly

by introducing spatial depth transformation convolution SPD-Conv to enhance multi-scale feature capture

the problem of low image resolution and difficult object detection could be solved. Subsequently

a lightweight upsampling operator DySample was introduced to optimize complex scene feature maps through dynamic sampling strategies

effectively mitigating target boundary blurring and environmental occlusion issues. Finally

the detection head would be changed to a lightweight shared convolutional detection head LSCD to reduce model parameters and computational complexity

while ensuring lightweight while improving detection accuracy. At the same time

the algorithm was comprehensively evaluated using quantitative evaluation indicators accuracy P

recall R

average accuracy mAP

and FLOPS on the publicly available infrared night scene dataset. The experimental results show that compared with the original algorithm

SDL-YOLO has a 1.5% increase in accuracy P

a 1.5% increase in recall R

and an 1.5% and 1.7% increase in average accuracy mAP50 and mAP50-95

respectively. At the same time

it has lower model complexity FLOPS. The effectiveness of SDL-YOLO algorithm in improving detection accuracy

correcting false positives and false negatives has been further demonstrated through visual analysis of detection results.

关键词

Keywords

references

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