IntroductionVehicle re-identification is a crucial task in intelligent transportation systems, presenting enduring challenges. The primary challenge involves the inefficiency of vehicle re-identification, necessitating substantial time for recognition within extensive datasets. A secondary challenge arises from notable image variations of the same vehicle due to differing shooting angles, lighting conditions, and diverse camera equipment, leading to reduced accuracy. This paper aims to enhance vehicle re-identification performance by proficiently extracting color and category information using a multi-attribute dense connection network, complemented by a distance control module.MethodsWe propose an integrated vehicle re-identification approach that combines a multi-attribute dense connection network with a distance control module. By merging a multi-attribute dense connection network that encompasses vehicle HSV color attributes and type attributes, we improve classification rates. The integration of the distance control module widens inter-class distances, diminishes intra-class distances, and boosts vehicle re-identification accuracy.ResultsTo validate the feasibility of our approach, we conducted experiments using multiple vehicle re-identification datasets. We measured various quantitative metrics, including accuracy, mean average precision, and rank-n. Experimental results indicate a significant enhancement in the performance of our method in vehicle re-identification tasks.DiscussionThe findings of this study provide valuable insights into the application of multi-attribute neural networks and deep learning in the field of vehicle re-identification. By effectively extracting color information from the HSV color space and vehicle category information using a multi-attribute dense connection network, coupled with the utilization of a distance control module to process vehicle features, our approach demonstrates improved performance in vehicle re-identification tasks, contributing to the advancement of smart city systems.

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基于多属性密集链接网络结合距离控制模块的车辆重识别方法

简介车辆重新识别是智能交通系统中的一项关键任务，带来了持久的挑战。主要挑战涉及车辆重新识别效率低下，需要大量时间在广泛的数据集中进行识别。第二个挑战是由于不同的拍摄角度、照明条件和不同的摄像设备，同一辆车的图像存在显着的变化，从而导致准确性降低。本文旨在通过使用多属性密集连接网络熟练地提取颜色和类别信息并辅以距离控制模块来提高车辆重新识别性能。方法我们提出了一种结合多属性密集连接的集成车辆重新识别方法具有距离控制模块的网络。通过合并包含车辆 HSV 颜色属性和类型属性的多属性密集连接网络，我们提高了分类率。距离控制模块的集成扩大了类间距离，减少了类内距离，提高了车辆重识别的准确性。结果为了验证我们方法的可行性，我们使用多个车辆重识别数据集进行了实验。我们测量了各种定量指标，包括准确度、平均精度和排名 n。实验结果表明我们的方法在车辆重新识别任务中的性能显着增强。讨论本研究的结果为多属性神经网络和深度学习在车辆重新识别领域的应用提供了有价值的见解。通过使用多属性密集连接网络有效地从 HSV 颜色空间和车辆类别信息中提取颜色信息，再加上利用距离控制模块来处理车辆特征，我们的方法证明了车辆重新识别任务中性能的提高，有助于推动智慧城市系统的进步。