Vehicle Logo Research Articles

Multi-Scale Vehicle Logo Detector

As the key information of vehicles, vehicle logo can assist in completing the identification of vehicle information. Therefore, the task of vehicle logo detection is of great practical significance. The existing object detection systems for vehicle logo detection cannot account for the detection accuracy of large and small-scale objects. Moreover, the accuracy of these methods can be further improved. In this study, we propose a new approach called multi-scale vehicle logo detector (SVLD), which is based on SSD. This method obtains better results than the current detection methods by setting the parameters of the preset boxes, changing the pre-training strategy, and adjusting the network structure. Experiments show that the proposed approach is better for multi-scale vehicle logo detection. Vehicle logos with large span size can be clearly detected, and the detection accuracy is substantially improved compared with those of other classic algorithms. For 512 × 512 input, SVLD obtains 3.1% improvement over the conventional methods and achieves a mean average precision (mAP) of 84.8% in the VLD-45 test set.

Vehicle logo detection based on deep convolutional networks

For intelligent transportation systems, vehicle logo is an important part in the research of vehicle information recognition. Deep convolutional neural networks (CNNs) have been more reasonable and stronger than artificial selection in feature extraction and expression for targets. In our previous work, we found that the network of feature extraction and the training policy of detection have great effects on the accuracy of vehicle logo. For improving small-scale object detecting precision, we change the training policy. According to our previous work, we propose a lightweight network structure with separable convolution to improve the real-time character for vehicle logo while implement the method in embedded devices. The experiment proves that our model can effectively improve the detection accuracy of vehicle logo. Our training policy is valid method for small-scale objects. The lightweight network can solve the equilibrium problem of detecting precision and speed.

RETRACTED: Research on vehicle license plate recognition technology based on deep convolutional neural networks

Convolutional Neural Networks (CNN) are used for visual type recognition. This is a first-order learning method. Types of Deep Neural Networks (DNNs) training to improve vehicle identification algorithms routinely. Symptoms are difficult to diagnose accurately. Recognition is done approximately in harsh environments. The recognition accuracy of Convolutional Neural Networks (CNN) is relatively high, which requires a large number of samples. A Vehicle Logo is the most explicit vehicle manufacturer indicator and is based on the Vehicle Logo Recognition (VMR) system. Mark recognition can still be a challenge because it is difficult to pinpoint the vehicle's identity in the image and seek stability in different imaging situations simultaneously. Vehicle Manufacturer Recognition (VMR) is proposed to eliminate the requirements for accurate identification and analysis of a Convolutional Neural Network (CNN) system. The lining is used based on information obtained from a CNN source. To train the neural network use the question expansion strategy to increase the training set of the synthetic transformation and to increase the rate at which the recall. This method can be maintained with high accuracy under light changes and noise conditions and is not adaptable to the environment. Experimental results show that data sets, which show that the algorithm is capable of strong generalizations, do not have high classification accuracy. The class's average accuracy will be higher than other methods; to show that CNN is better at recognizing vehicle identification with the results of comparative tests.

Vehicle logo recognition method based on feature enhancement

Vehicle logo recognition method based on feature enhancement

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we requirea quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by histograms of oriented gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.

Single Stage Vehicle Logo Detector Based on Multi-Scale Prediction

Single Stage Vehicle Logo Detector Based on Multi-Scale Prediction

Brand Detection System using Deep Learning

In this paper a method of recognizing logos of the brand of cosmetic products using deep learning. There are several of hoax product which easily copies the famous brand’s logo and deteriorates the company’s image. The machine learning has proved to be useful in various of the fields like medical, object detection, vehicle logo recognitions. But till now very few of the works have been performed in cosmetic field. This field is covered using the model sequential convolutional neural network using Tensorflow and Keras. For the visual representation of the result Tensorboard is used. Work have been started with two of the brands-Lakme and L’Oreal. Depending upon the success of this technique, further brands for logo may be added for recognition. The accuracy of approximately 80% was obtained using this technique.

Vehicle Logo Recognition with Small Sample Problem in Complex Scene Based on Data Augmentation

Automatic identification for vehicles is an important topic in the field of Intelligent Transportation Systems (ITS), and the vehicle logo is one of the most important characteristics of a vehicle. Therefore, vehicle logo detection and recognition are important research topics. Because of the problems that the area of a vehicle logo is too small to be detected and the dataset is too small to train for complex scenes, considering the speed of recognition and the robustness to complex scenes, we use deep learning methods which are based on data optimization for vehicle logo in complex scenes. We propose three augmentation strategies for vehicle logo data: cross-sliding segmentation method, small frame method, and Gaussian Distribution Segmentation method. For the problem of small sample size, we use cross-sliding segmentation method, which can effectively increase the amount of data without changing the aspect ratio of the original vehicle logo image. To expand the area of the logos in the images, we develop the small frame method which improves the detection results of the small area vehicle logos. In order to enrich the position diversity of vehicle logo in the image, we propose Gaussian Distribution Segmentation method, and the result shows that this method is very effective. The F1 value of our method in the YOLO framework is 0.7765, and the precision is greatly improved to 0.9295. In the Faster R-CNN framework, the F1 value of our method is 0.7799, which is also better than before. The results of experiments show that the above optimization methods can better represent the features of the vehicle logos than the traditional method, and the experimental results have been improved.

A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

In this paper, we present a novel learning-based scheme for vehicle logo recognition (VLR). This scheme is termed Multilayer Pyramid Network Based on Learning (MLPNL) and is based on the principle that considering multiple resolutions is helpful for extracting valuable features that benefit the final recognition performance. The innovations of this scheme include (1) a multilayer pyramid network, with pixel difference matrices (PDMs) as its input and output and feature parameters mapping one PDM to another; (2) an objective function and a corresponding optimization method designed to facilitate the learning of the feature parameters of the proposed multilayer pyramid network; and (3) a multi-codebook-based encoding method that makes best use of the features extracted from PDMs corresponding to different resolutions. Extensive experiments conducted with an open dataset, HFUT-VL, demonstrate that the proposed MLPNL scheme outperforms state-of-the-art handcrafted descriptors and non-deep-learning-based learning methods when fewer training samples exist. Experiments conducted with a benchmark dataset, XMU, demonstrate that MLPNL outperforms existing state-of-the-art VLR methods. Experiments conducted both on HFUT-VL and XMU demonstrate that MLPNL is faster than most deep-learning-based learning methods while maintaining nearly the same recognition rate. Code has been made available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HFUT-CV/MLPNL</uri> .

Detecting Motion Blurred Vehicle Logo in IoV Using Filter-DeblurGAN and VL-YOLO

Extracting vehicle information is of great significance to the construction of the Internet of Vehicles (IoV). Vehicle logo detection (VLD) technology can effectively extract vehicle information. Due to the complex traffic environment, existing methods have difficulty to accurately detect the vehicle logo, especially when the vehicle logo has motion blur. To alleviate these problems, a new approach is proposed to detect vehicle logo under motion blur with the combination of Filter-DeblurGAN and VL-YOLO. Filter-DeblurGAN possesses a judgment mechanism, which can determine whether the image needs to be deblurred based on the degree of blur of each image. It can also deblur images with arbitrary resolution. Filter-DeblurGAN solves the defect that DeblurGAN lacks the judgment mechanism and is too harsh on resolution. It can be directly applied to the deblurring process of VLD. We also propose a method using VL-YOLO and Eliminate Outliers Cluster (EOC) algorithm to further improve the detection accuracy. VL-YOLO achieves accurate detection of the vehicle logo by constructing a deeper multi-scale detection network and using the initial candidate boxes provided by EOC algorithm. The EOC algorithm is not affected by outliers. In order to further promote the research of VLD, we have labeled a new vehicle logo dataset named LOGO-17, which contains 17 different categories of vehicle logos. The experimental results demonstrate that the proposed method achieves good detection accuracy in the environment of motion blur, and outperforms existing methods.

Research and Implementation of Vehicle Target Detection and Information Recognition Technology Based on NI myRIO.

A vehicle target detection and information extraction scheme based on NI (National Instruments) myRIO is designed in this paper. The vehicle information acquisition and processing method based on image recognition is used to design a complete vehicle detection and information extraction system. In the LabVIEW programming environment, the edge detection method is used to realize the vehicle target detection, the pattern matching method is used to realize the vehicle logo recognition, and the Optical Character Recognition (OCR) character recognition algorithm is used to realize the vehicle license plate recognition. The feasibility of the design scheme in this paper is verified through the actual test and analysis. The scheme is intuitive and efficient, with the high recognition accuracy.

A Multi-Feature Fusion Based Vehicle Logo Recognition Approach for Traffic Checkpoint

In this paper, we propose a vehicle logo recognition algorithm based on multifeature fusion using a hierarchical classification approach, which can be applied at traffic checkpoints. First, a typical database of vehicle logos is set up based on surveillance images recorded at traffic checkpoints. Next, three features, HOG, Curvature histograms, and GIST, are extracted and three corresponding first level classifiers are trained using the support vector machine (SVM) algorithm. The probability that a certain test sample belongs to a certain kind can be obtained by predicting the sample with each level-1 classifier. All these probabilities are then concatenated and used as features for training a second-level SVM classifier. The resultant new classifier is used for classifying the vehicle logos of the test set. The experimental results show that the proposed approach to hierarchically integrate multiple features provides excellent accuracy for the vehicle logo recognition task.

Vehicle Logo Recognition Based on Enhanced Matching for Small Objects, Constrained Region and SSFPD Network.

Vehicle Logo Recognition (VLR) is an important part of vehicle behavior analysis and can provide supplementary information for vehicle identification, which is an essential research topic in robotic systems. However, the inaccurate extraction of vehicle logo candidate regions will affect the accuracy of logo recognition. Additionally, the existing methods have low recognition rate for most small vehicle logos and poor performance under complicated environments. A VLR method based on enhanced matching, constrained region extraction and SSFPD network is proposed in this paper to solve the aforementioned problems. A constrained region extraction method based on segmentation of the car head and car tail is proposed to accurately extract the candidate region of logo. An enhanced matching method is proposed to improve the detection performance of small objects, which augment each of training images by copy-pasting small objects many times in the unconstrained region. A single deep neural network based on a reduced ResNeXt model and Feature Pyramid Networks is proposed in this paper, which is named as Single Shot Feature Pyramid Detector (SSFPD). The SSFPD uses the reduced ResNeXt to improve classification performance of the network and retain more detailed information for small-sized vehicle logo detection. Additionally, it uses the Feature Pyramid Networks module to bring in more semantic context information to build several high-level semantic feature maps, which effectively improves recognition performance. Extensive evaluations have been made on self-collected and public vehicle logo datasets. The proposed method achieved 93.79% accuracy on the Common Vehicle Logos Dataset and 99.52% accuracy on another public dataset, respectively, outperforming the existing methods.

A Deep Learning Framework for Joint Image Restoration and Recognition

Image restoration and recognition are important computer vision tasks representing an inherent part of autonomous systems. These two tasks are often implemented in a sequential manner, in which the restoration process is followed by a recognition. In contrast, this paper proposes a joint framework that simultaneously performs both tasks within a shared deep neural network architecture. This joint framework integrates the restoration and recognition tasks by incorporating: (i) common layers, (ii) restoration layers and (iii) classification layers. The total loss function combines the restoration and classification losses. The proposed joint framework, based on capsules, provides an efficient solution that can cope with challenges due to noise, image rotations and occlusions. The developed framework has been validated and evaluated on a public vehicle logo dataset under various degradation conditions, including Gaussian noise, rotation and occlusion. The results show that the joint framework improves the accuracy compared with the single task networks.

Vehicle logo recognition based on local quantization of enhanced edge gradient features

Vehicle logo recognition based on local quantization of enhanced edge gradient features

Fast vehicle logo detection in complex scenes

Vehicle logo detection has received considerable attention due to its many applications, such as brand reputation measurement and vehicle monitoring. Deep-learning algorithms, such as you only look once (YOLO) and faster-regional convolutional neural network, have obtained excellent performance for object detection. However, it is not easy to exploit these methods to detect small objects. This work presents a novel method for fast and accurate vehicle logo detection (VLD) in complex scenes. First, we modify the original YOLOv3 model for the VLD task and solve the small object detection problem by hard example training. Second, we construct a new VLD dataset known as VLD-30, which encourages us to develop a data-driven training method and improve the detection accuracy. Experimental results demonstrate that the proposed data-training method is useful and the modified YOLOv3 is effective for fast and accurate vehicle logo detection in complex scenes.

Vehicle logo recognition based on overlapping enhanced patterns of oriented edge magnitudes

Vehicle logo recognition (VLR) has attracted wide attention from the community of intelligent transportation systems (ITS) due to its important role. Although many methods have been proposed for VLR, it remains a challenging problem. In this paper, we present a novel method for VLR. Our method includes (1) observation of the local anisotropy of vehicle logo images; (2) adoption of the idea of patterns of oriented edge magnitudes (POEM) and an advanced version of POEM for vehicle logo feature description called overlapping enhanced POEM (OE-POEM); (3) implementation of whitened principal component analysis (WPCA) for feature dimension reduction followed by collaborative-representation-based classification (CRC) as a classifier to perform VLR. We also construct a new vehicle logo dataset (HFUT-VL), which is larger and more comprehensive than the existing vehicle logo datasets. Finally, we conduct experiments on HFUT-VL, and the results indicate state-of-the-art VLR performance.

A Vehicle Logo Location Method Based on Sub-block Pixel Intensity and Weight Matrix

Vehicle logo, as the key information of vehicle, combined with other vehicle characteristics will make vehicle management more effective in the intelligent transportation system. However, it is still a challenging task to detect vehicle logo due to its variations in geometry, location and illumination. In this paper, we propose a method to locate the texture type, which is based on the sub-block pixel intensity and weight matrix. This method is suitable for the target location under various backgrounds, and has a faster location speed in the complex background. It has a high robustness and strong anti-interference to the illumination. Finally, our method was verified effectively through the experiment compared to other methods.

Hyper‐parameters optimisation of deep CNN architecture for vehicle logo recognition

The training of deep convolutional neural network (CNN) for classification purposes is critically dependent on the expertise of hyper-parameters tuning. This study aims to minimise the user variability in training CNN by automatically searching and optimising the CNN architecture, particularly in the field of vehicle logo recognition system. For this purpose, the architecture and hyper-parameters of CNN were selected according to the implementation of the stochastic method of particle swarm optimisation on the training–testing data. After obtaining the optimised hyper-parameters, the CNN is fine-tuned and trained to ensure better network convergence and classification performance. In this study, a total of 14,950 vehicle logo images are divided into two independent training and testing sets. In addition, these images are segmented coarsely, thus the requirement of precise logo segmentation is obviated in this work. The learned features of the CNN were sufficiently discriminative to be classified using multiclass Softmax classifier. With implementation using a graphics processing unit (GPU), the computation time of the proposed method is acceptable for real-time application. The experimental results explicitly prove that the authors’ approach outperforms most of the state-of-the-art methods, achieving an accuracy of 99.1% over 13 vehicle manufacturers.

Vehicle logo recognition using whitening transformation and deep learning

This paper presents a vehicle logo recognition using a deep convolutional neural network (CNN) method and whitening transformation technique to remove redundancy of adjacent image pixels. Backpropagation algorithm with stochastic gradient descent optimization technique has been deployed to train and obtain weight filters of the networks. Seven layers of our proposed CNN incorporating an input layer, five hidden layers and an output layer have been implemented to capture rich and discriminative information of vehicle logo images. Functioning as the output layer of the network, the softmax classifier is utilized to handle multiple classes of vehicle logo image. For a given vehicle logo image, the network provides the probability for each vehicle manufacturer to which the given logo image belongs. Unlike most of the common traditional methods that employ handcrafted visual features, our proposed method is able to automatically learn and extract high-level features for the classification task. The extracted features are discriminative sufficiently to perform well in various imaging conditions and complex scenes. We validate our proposed method by utilizing a public vehicle logo image dataset, which comprises 10,000 and 1500 vehicle logo images for training and validation objective, respectively. Experimental results based on our proposed method outperform other existing methods in terms of the computational cost and overall classification accuracy of 99.13%.