基于变压器的车辆颜色细粒度分类神经网络

Yingjin Wang, Chuanming Wang, Yuchao Zheng, Huiyuan Fu, Huadong Ma
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引用次数: 0

摘要

车辆颜色识别技术的发展对车辆识别和智能交通系统的发展具有重要意义。然而,由于车辆颜色种类少,且受环境光照的影响,使得细粒度车辆颜色识别成为一项具有挑战性的任务。以前的数据集训练数据不足,颜色分类少,导致识别精度低,在实际使用中缺乏灵活性。同时,低效的特征学习也导致了以往方法的识别性能不佳。因此,我们从车辆刺刀监测中收集后方拍摄数据集,用于细粒度车辆颜色识别。根据所提出的标注算法,可以将图像划分为11个主要类别和75个颜色子类别,该算法可以消除光照的影响,并为每张图像分配颜色注释。提出了一种新的识别模型,可以有效地识别车辆颜色。我们巧妙地将Transformer插值到识别模型中,以增强传统神经网络的特征学习能力,并通过对所提出的数据集的深入分析,专门设计了一个分层损失函数。我们在数据集上对设计的识别模型进行了评估,其准确率达到97.77%,优于传统方法。
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Transformer based Neural Network for Fine-Grained Classification of Vehicle Color
The development of vehicle color recognition technology is of great significance for vehicle identification and the development of the intelligent transportation system. However, the small variety of colors and the influence of the illumination in the environment make fine-grained vehicle color recognition a challenge task. Insufficient training data and small color categories in previous datasets causes the low recognition accuracy and the inflexibility of practical using. Meanwhile, the inefficient feature learning also leads to poor recognition performance of the previous methods. Therefore, we collect a rear shooting dataset from vehicle bayonet monitoring for fine-grained vehicle color recognition. Its images can be divided into 11 main-categories and 75 color subcategories according to the proposed labeling algorithm which can eliminate the influence of illumination and assign the color annotation for each image. We propose a novel recognition model which can effectively identify the vehicle colors. We skillfully interpolate the Transformer into recognition model to enhance the feature learning capacity of conventional neural networks, and specially design a hierarchical loss function through in-depth analysis of the proposed dataset. We evaluate the designed recognition model on the dataset and it can achieve accuracy of 97.77%, which is superior to the traditional approaches.
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