Liu Mengxue, Min Weidong, Han Qing, Xiang Hongyue, Zhu Meng
School of Mathematics and Computer Science, Nanchang University, Nanchang, 330031, China.
Institute of Metaverse, Nanchang University, Nanchang, 330031, China.
Sci Rep. 2024 Nov 3;14(1):26531. doi: 10.1038/s41598-024-77973-8.
Vehicle re-identification (Vehicle Re-ID) aims at retrieving and tracking the specified target vehicle with multiple other cameras, which can provide help in checking violations and catching fugitives, but there are still the following problems that need to be solved urgently. First, the existing collected Vehicle Re-ID data often have low resolution and blur in local regions, so that the Vehicle Re-ID algorithm cannot accurately extract subtle feature representations. In addition, small features are easy to cause the disappearance of features under the operation of a large convolution kernel, which makes the model unable to capture and learn subtle features, resulting in inaccurate judgment of vehicles. In this study, we propose a Vehicle Re-ID method based on super resolution and pyramidal convolution residual network. Firstly, a super-resolution image generation network leveraging generative adversarial networks (GANs) is proposed. This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. Moreover, the concept of residual learning is applied between the multi levels of pyramidal convolution operations to expedite model optimization and enhance recognition capabilities. Ultimately, the double pyramidal convolutions are meticulously employed on both the original image and the super-resolution image, yielding low-noise feature representations and intricate semantic information respectively. By seamlessly fusing these two diverse sources of information, the resultant combined features exhibit heightened discrimination capabilities and significantly bolster the robustness of image features. In order to verify the effectiveness of the proposed method, extensive experiments are carried out on VeRi-776 and VehicleID datasets. The experimental results show that the method proposed in this paper effectively captures the detail information of vehicle images, accurately distinguishes the subtle differences between different vehicles of the same type, and is superior to state-of-the-art methods.
车辆重新识别（Vehicle Re-ID）旨在利用多个其他摄像头检索和跟踪指定的目标车辆，这在检查违规行为和追捕逃犯方面能提供帮助，但仍存在以下亟待解决的问题。首先，现有的收集到的车辆重新识别数据往往分辨率低分辨率低且局部区域模糊，使得车辆重新识别算法无法准确提取细微的特征表示。此外，小特征在大卷积核操作下容易导致特征消失，这使得模型无法捕捉和学习细微特征，从而导致对车辆的判断不准确。在本研究中，我们提出了一种基于超分辨率和金字塔卷积残差网络的车辆重新识别方法。首先，提出了一种利用生成对抗网络（GANs）的超分辨率图像生成网络。该网络采用内容损失和对抗损失作为优化标准，确保从低分辨率图像到超分辨率图像的高效转换，同时精心保留复杂的高频细节。然后，设计多级金字塔卷积操作以生成多尺度特征，能够捕捉不同尺度的信息。此外，在多级金字塔卷积操作之间应用残差学习概念，以加快模型优化并增强识别能力。最终，在原始图像和超分辨率图像上都精心使用双金字塔卷积，分别产生低噪声特征表示和复杂语义信息。通过无缝融合这两种不同的信息源，得到的组合特征具有更高的辨别能力，并显著增强了图像特征的鲁棒性。为了验证所提方法的有效性，在VeRi-776和VehicleID数据集上进行了广泛的实验。实验结果表明，本文提出的方法有效地捕捉了车辆图像的细节信息，准确地区分了同一类型不同车辆之间的细微差异，并且优于现有最先进的方法。
