Hierarchical System for Car Make and Model Recognition on Image Using Neural Networks

Fomin, Ivan; Nenahov, Ivan; Bakhshiev, Aleksandr

doi:10.1109/icieam48468.2020.9112026

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Fomin et al [ 23 ] presented a system for car recognition based on cascaded classifiers: they first identify the car orientation with a VGG-16 neural network [ 20 ], and subsequently select a different car-model classifier based on the found orientation, using an Inception-ResNet-v3 [ 24 ]. Following a similar concept, we will also experiment with a cascade of classifiers, although based on different abstractions: car type and car year of release.…”

Section: Car Classification Datasetsmentioning

confidence: 99%

“…Table 2 offers a comparison between the three methods for hierarchical car classification presented in this paper (AVA, 2SC, and HML), and existing solutions designed or adapted to the CompCars dataset, as presented in Section 2 : CMP [ 18 ], ABN [ 22 ], and the method by Fomin et al [ 23 ]. Of the latter group, only Fomin et al exploit a version of hierarchical classification, in the form of a cascade of classifiers for car orientation and car make.…”

Section: Car Classificationmentioning

confidence: 99%

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Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

Buzzelli

Segantin

2021

Sensors

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We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.

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Section: Car Classification Datasetsmentioning

confidence: 99%

Section: Car Classificationmentioning

confidence: 99%

Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

Buzzelli

Segantin

2021

Sensors

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“…VMMR recreates an essential position in intelligent transportation systems (ITS) and automated vehicular surveillance (AVS), such as vehicle management, illegal hit and run, self-driving, auto-parking, automated tolling systems, and traffic surveillance. The main challenge in decoding this trouble lies in the significant variability of vehicle models, which significantly increases the variety of images [1]. Many features can be extracted to recognize a car from an image, such as license plate, headlights, tires size.…”

Section: Introductionmentioning

confidence: 99%

Car make and model recognition using convolutional neural network: fine-tune AlexNet architecture

Asifur Rahman Khan,

Talukder,

Anwar Hossen

et al. 2024

IJEECS

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Artificial intelligence (AI) has significantly contributed to car make and model recognition in this current era of intelligent technology. By using AI, it is much easier to identify car models from any picture or video. This paper introduces a new model by fine-tuning the AlexNet architecture to determine the car model from images. First of all, our car image dataset has been created. Some of these images were taken by us, and others were taken from the website of the car connection. Then we cleaned all the unwanted images for better performance. Our dataset has ten classes containing 5,000 car images split into train and test data. After that, we augmented our data with random flip, rotation, and zoom to reduce overfitting. Finally, we used a pre-trained convolutional neural network (CNN) model AlexNet architecture. We fine-tuned AlexNet (FT-AlexNet) by adding three extra layers for better classification and compared it with the original AlexNet. To measure the performance of these models, accuracy, precision, recall, and F1-score were used. The results show that fine-tune AlexNet architecture outperforms the original AlexNet architecture. The results prove that recognition accuracy has increased due to our improvement approach.

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Car make and model recognition system using rear-lamp features and convolutional neural networks

Bularz,

Przystalski,

Ogorzałek

2023

Multimed Tools Appl

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Recognizing cars based on their features is a difficult task. We propose a solution that uses a convolutional neural network (CNN) and image binarization method for car make and model classification. Unlike many previous works in this area, we use a feature extraction method combined with a binarization method. In the first stage of the pre-processing part we normalize and change the size of an image. The image is then used to recognize where the rear-lamps are placed on the image. We extract the region and use the image binarization method. The binarized image is used as input to the CNN network that finds the features of a specific car model. We have tested the combinations of three different neural network architectures and eight binarization methods. The convolutional neural network with parameters of the highest quality metrics value is used to find the characteristics of the rear lamps on the binary image. The convolutional network is tested with four different gradient algorithms. We have tested the method on two data sets which differ in the way the images were taken. Each data set consists of three subsets of the same car, but is scaled to different image dimensions. Compared to related works that are based on CNN, we use rear view images in different position and light exposure. The proposed method gives better results compared to most available methods. It is also less complex, and faster to train compared to other methods. The proposed approach achieves an average accuracy of 93,9% on the first data set and 84,5% on the second set.

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Hierarchical System for Car Make and Model Recognition on Image Using Neural Networks

Cited by 5 publications

References 8 publications

Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

Car make and model recognition using convolutional neural network: fine-tune AlexNet architecture

Car make and model recognition system using rear-lamp features and convolutional neural networks

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