Image Quality Assessment for Specific Areas of Traffic Targets

LU Zhaohui; QI Guodong; YU Huimin; YAN Yuming

doi:10.13203/j.whugis20230192

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Image Quality Assessment for Specific Areas of Traffic Targets

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- Image Quality Assessment for Specific Areas of Traffic Targets
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 10, Pages: 2064-2071(2025)
- 作者机构：
  
  浙江大学信息与电子工程学院，浙江杭州，310013
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20230192
  CLC： P237
- Received：18 May 2024，
  
  Published：05 October 2025
- 稿件说明：
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卢朝晖,齐国栋,于慧敏，等.交通目标特定区域的图像质量评估[J].武汉大学学报（信息科学版）,2025,50(10):2064-2071.

LU Zhaohui,QI Guodong,YU Huimin,et al.Image Quality Assessment for Specific Areas of Traffic Targets[J].Geomatics and Information Science of Wuhan University,2025,50(10):2064-2071.
卢朝晖,齐国栋,于慧敏，等.交通目标特定区域的图像质量评估[J].武汉大学学报（信息科学版）,2025,50(10):2064-2071. DOI：10.13203/j.whugis20230192

LU Zhaohui,QI Guodong,YU Huimin,et al.Image Quality Assessment for Specific Areas of Traffic Targets[J].Geomatics and Information Science of Wuhan University,2025,50(10):2064-2071. DOI：10.13203/j.whugis20230192

摘要

针对交通监控场景下复杂道路环境干扰和真实参考图像缺失等因素导致的图像质量评估不佳问题，提出了一种两阶段图像质量评估方法，用于感兴趣交通目标中特定区域的图像质量评估。首先，为了减少背景等图像上下文的影响，提出使用目标检测算法提取图像中的交通目标，并根据轮廓、几何特征和车牌识别算法识别感兴趣交通目标中的特定区域；其次，为了弥补特定区域中缺失真实参考图像的问题，提出使用生成对抗网络生成伪参考图，设计一种新的损失函数，对生成对抗网络和图像质量评估器进行协同训练。在交通监控场景下的图像质量评估数据集上，对所提方法进行了评估实验。与其他图像质量评估方法相比较，所提方法模型小，并在斯皮尔曼秩相关系数和皮尔逊线性相关系数上均处于最优水平。因此，所提出的两阶段图像质量评估方法可以应用于复杂交通场景下的图像质量评估。

Abstract

Objectives

The study of image quality evaluation is aimed at the problems of poor image quality evaluation caused by the interference of complex road environment and the absence of real reference images in traffic monitoring scenarios.

Methods

We propose a two-stage image quality assessment method for a specific area of the traffic target of interest. First

in order to reduce the influence of image context such as background

an object detection algorithm is proposed to extract the traffic target in the image

and the specific area of the interested traffic target is identified according to the contour

geometric features and license plate recognition algorithm. Second

in order to make up for the lack of real reference images in a specific region

a novel loss function is proposed to generate pseudo-reference images using generative adversarial networks

and to train the generative adversarial networks and image quality evaluators together.

Results

The proposed algorithm is evaluated on the dataset of image quality evaluation in traffic monitoring scenario. Compared with other image quality assessment methods

the model of this method is small

and it is at the optimal level in Spearman rank correlation coefficient index and Pearson linear correlation coefficient index.

Conclusions

The two-stage image quality assessment method can provide an effective method for the practical application of image quality assessment in complex traffic scenarios.

关键词

Keywords

references

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