| 韩 浩,何赟泽,杜 旭,王洪金.基于偏振信息图像增强的多目标检测[J].电子测量与仪器学报,2023,37(3):29-38 |
| 基于偏振信息图像增强的多目标检测 |
| Multi-object detection based on polarization information image enhancement |
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| DOI： |
| 中文关键词: 偏振成像 去雾 YOLO v5 目标检测 |
| 英文关键词:polarization imaging dehazing YOLO v5s object detection |
| 基金项目:湖南省自然科学基金重大项目(2021JC0004)资助 |
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| 中文摘要: |
| 偏振是光的重要特性之一,偏振成像技术能够获取场景中目标的强度信息和偏振信息,偏振信息能够反映出目标物体
表面的材质特征。 本文针对雾霾天气状况下道路场景中常见目标识别结果的准确性要求,提出了两种基于偏振信息的图像增
强方案。 首先经过多次采集实验,经过数据清洗、图像标注构建偏振数据集,共 4 649 张图像和 31 877 个标签。 针对雾霾轻度
污染的场景,通过区域自动生长算法分割出偏振强度图像中的天空区域,根据天空区域的偏振度和偏振角信息以及大气物理散
射模型反演出目标反射光,从而实现图像去雾。 针对雾霾重度污染的场景,使用小波变换的方式对图像进行增强,利用偏振度
图像来增强强度图像中的目标轮廓。 使用图像灰度方差和图像信息熵作为图像质量评价指标,使用 YOLO v5s 深度学习网络
进行目标检测。 实验结果表明,雾霾轻度污染的情况下,图像质量和目标检测准确性均有所提升,图像信息熵提升了 3. 36%,灰
度方差提升了 40. 27%,目标检测 mAP 达到了 76. 40%,提升了 12. 69%;雾霾重度污染的情况下,目标检测 mAP 提升约 1. 69%。 |
| 英文摘要: |
| Polarization is one of the important characteristics of light. Polarization imaging technology can obtain the intensity information
and polarization information of the target in the scene. Polarization information can reflect the material characteristics of the target
surface. In this paper, two image enhancement schemes based on polarization information are proposed to meet the accuracy requirements
of common target recognition results in road scenes under haze weather conditions. First of all, the polarization data set is constructed
through multiple acquisition experiments, data cleaning and image labeling, with a total of 4 649 images and 31 877 tags. For the scene
with slight haze pollution, the sky region in the polarization intensity image is segmented by the region automatic growth algorithm, and
the reflected light of the target is reversely generated according to the polarization degree and polarization angle information of the sky
region and the atmospheric physical scattering model, so as to realize the image defogging. For the heavily polluted scene of haze,
wavelet transform is used to enhance the image, and the degree of polarization image is used to enhance the target contour in the intensity
image. The image gray variance and image information entropy are used as image quality evaluation indicators, and the YOLO v5s deep
learning network is used for object detection. The results show that in the case of light haze pollution, the image quality and object
detection accuracy have been improved, the image information entropy has increased by 3. 36%, the gray variance has increased by
40. 27%, and the object detection mAP has reached 76. 40%, increased by 12. 69%. In the case of heavy smog pollution, the object
detection mAP increased by about 1. 69%. |
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