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Received：23 June 2020，
Revised：2020-8-31，
Accepted：07 September 2020，
Published：16 May 2021
移动端阅览
目的
2
小样本情况下的车标识别在实际智能交通系统中具有十分重要的应用价值。针对从实际监控系统中获取的车标图像低分辨率、低质量的特点，考虑如何从车标结构相似性、局部显著特征方面来对车标的整体特征进行增强，提出一种特征增强策略驱动下的车标识别方法（vehicle logo recognition method based on feature enhancement，FE-VLR）。
方法
2
提取车标图像的自对称相似特征，构建图像金字塔，在每层金字塔下提取车标的整体特征和局部显著特征，其中局部显著区域通过基于邻域块相关度的显著区域检测来获取，最后结合CRC（collaborative representation based classification）分类器对车标进行分类识别。
结果
2
在公开车标数据集HFUT-VL（Vehicle Logo Dataset from Hefei University of Technology）和XMU（Xiamen University Vehicle Logo Dataset）上对算法效果进行评估，实验结果表明，在小样本情况下，本文方法优于其他一些传统的车标识别方法，且与一些基于深度学习模型的方法相比，其识别率也有所提升。在HFUT-VL数据集上，当训练样本数为5时，识别率达到97.78%；当训练样本数为20时，识别率为99.1%。在更为复杂的XMU数据集上，本文方法表现出了更好的有效性和更强的鲁棒性，当训练样本在15幅及以下时，本文方法与具有较好表现的OE-POEM（overlapping enhanced patterns of oriented edge magnitudes）算法相比至少提升了7.2%。
结论
2
本文提出的基于特征增强策略的车标识别方法，通过融合自对称相似特征、局部显著特征和车标整体特征来增强特征的表达，提高了对实际道路中的低质量、低分辨率车标图像的识别能力，更能满足实际应用中对车标识别的需求。
Objective
2
With the rapid development of computer vision technology
the demand for intelligent and humanized transportation systems is gradually increasing. Vehicle logo recognition (VLR) is an important part of intelligent transportation systems
and the requirements for its recognition effect are gradually increasing. Considering the difficulty in achieving the samples of some vehicle logos from real surveillance systems in certain areas and the cost of collecting samples and training
the recognition of vehicle logos under small training samples still has very important application value. Vehicle logos captured from real surveillance systems on the road suffer from the following characteristics: 1) low resolution
2) easy to blur due to the movement of vehicles
and 3) easily influenced by light from the environment. Thus
the recognition of vehicle logos is still a challenging problem. Given the fact that some vehicle logos have similar structures and part of the vehicle logos has salient features
we consider how to enhance the overall characteristics of vehicle logos from the aspects of symmetrical structural and local saliency
which can benefit VLR
and propose a VLR method based on feature enhancement
called feature enhancement-based vehicle logo recognition (FE-VLR).
Method
2
FE-VLR comprehensively considers the structural similarity features and local salient features of the vehicle logo and then combines them together with the overall features of the vehicle logo to identify the vehicle logo. Based on the analysis of the structural symmetry of the left and right parts of the vehicle logo
this study calculates the similarity value of the image block to express the similarity feature. In addition
a method for calculating salient regions based on the correlation of neighborhood blocks is proposed to locate and extract the salient features of the vehicle logo. First
it extracts similar self-symmetrical features of vehicle logo images and then builds an image pyramid. Under each layer of the pyramid
the overall features and local salient features of the vehicle logos are extracted. Local salient locations are obtained by salient region detection based on the correlation of neighborhood blocks. Finally
a collaborative representation-based classification (CRC) classifier is used to classify the vehicle logos. CRC is a fast and effective classifier suitable for small training samples. The classifier uses the collaborative coding of all samples in the sample dictionary to represent the prediction samples
so as to improve the recognition rate by using the difference of the same attribute between different types of vehicle logos.
Result
2
The effectiveness of our algorithm is evaluated on the public vehicle logo datasets Vehicle Logo Dataset from Hefei University of Technology (HFUT-VL) and Xiamen University Vehicle Logo Dataset (XMU). The experimental results show that under small training samples
FE-VLR is superior to some other traditional VLR methods and also has higher recognition rates than some convolutional neural network-based methods. On the HFUT-VL dataset
when the number of training samples is 5
the recognition rate of FE-VLR reaches 97.78%
and when the number of training samples is 20
the recognition rate reaches 99.1%. On the more complex XMU dataset
FE-VLR is more efficient and robust. When the number of training samples is equal or less than 15
FE-VLR can improve the recognition rate by at least 7.2% compared with overlapping enhanced patterns of oriented edge magnitudes(OE-POEM). The experimental results show that FE-VLR always has better performance under small samples.
Conclusion
2
The FE-VLR method increases the recognition ability of low-quality and low-resolution vehicle logo images obtained from real surveillance systems on the road
which can better meet the needs of VLR in practical applications. The experimental results on the public HFUT-VL and XMU datasets show that in case of small samples
the recognition rate of FE-VLR is higher than that of other VLR methods and better than some recognition methods based on deep learning models.
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