Vehicle images dataset for make and model recognition

Ali, Mohsin; Tahir, Muhammad Atif; Durrani, Muhammad

doi:10.1016/j.dib.2022.108107

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…We used images of common cars running on the road of Pakistan [13]. There are 3,103 and 752 training and test images divided into 48 car models/classes.…”

Section: Methods 21 Datasetmentioning

confidence: 99%

Vehicle make and model recognition using mixed sample data augmentation techniques

Anwar

Zakir²

2023

IJ-AI

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<span lang="EN-US">Vehicle identification based on make and model is an integral part of an intelligent transport system that helps traffic monitoring and crime control. Much research has been performed in this regard, but most of them used manual feature extraction or ensemble convolution neural networks that result in increased execution time during inference. This paper compared three deep learning models and utilized different augmentation techniques to achieve state-of-the-art performance without ensembling or fusing the models. Experimentations are made without any augmentation, with standard augmentation, and by mixed sample data augmentation techniques. Gradient accumulation and stochastic weighted averaging with mixed precision are used to have a large batch size that helped to reduce training time. The dataset comprised 48 vehicles’ models running on the road of Pakistan. The highest accuracy and F1 score of 97% and 95% using the FMix augmentation technique with EfficientNetV2-S architecture gave the confidence that the proposed solution can be implemented in production. </span>

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“…We used images of common cars running on the road of Pakistan [13]. There are 3,103 and 752 training and test images divided into 48 car models/classes.…”

Section: Methods 21 Datasetmentioning

confidence: 99%

Vehicle make and model recognition using mixed sample data augmentation techniques

Anwar

Zakir²

2023

IJ-AI

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“…The images and/or video captured are that of varied illumination, background, and views making the data challenging to learn [14]. With the region of interest extracted and labeled with key significant features extracted, there can be an improvement in learning as seen in many states of art concerning segmentation and classification [17].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Instance Segmentation Polygonal Dataset for Private Surveillance System

Ottakath¹,

Al-Maadeed²

2023

Preprint

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Vehicle identification is an important task in traffic monitoring because it allows for efficient inference and provides a cause for action. Vehicle classification via deep learning and other approaches such as segmentation is a critical tool for re-identification. In this paper, instance segmentation is used to identify vehicle makes with license plate detection, allowing for better unique vehicle recognition for re-identification. A dataset is annotated and modified, for example, by segmenting it with polygonal bounding boxes that capture the vehicle's unique frontal features. In addition, license plate localization is performed. The results showed improved classification as well as a high mAP for the dataset when compared to previous approaches based on CNN and deformed CNN. Furthermore, a deep residual network and fully connected layer-based classification were utilized as the backbone for feature representation. Instance segmentation detects objects by segmenting and classifying regions of interest. The imbalance in the dataset is resolved using a mosaic-tiled approach, which produces greater precision than other approaches.

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Section: Introductionmentioning

confidence: 99%

Polygonal Dataset for Vehicle Instance Segmentation for Private Surveillance System

Ottakath¹,

Al-Maadeed²

2023

Preprint

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Vehicle identification is an important task in traffic monitoring because it allows for efficient inference and provides a cause for action. Vehicle classification via deep learning and other approaches such as segmentation is a critical tool for re-identification. In this paper, instance segmentation is used for vehicle make identification with license plate detection, allowing for better unique vehicle recognition for re-identification. An existing dataset is re-annotated and modified for polygonal segmentation of the vehicle’s unique frontal features, resulting in representation of the vehicle with its frontal form learned. In addition, an additional license plate identification class is added for efficient re-identification further down the re-identification and tracking pipeline. Furthermore, an additional class of license plate identification is added for efficient re-identification further down the re-identification and tracking pipeline. The results showed improved classification as well as a high mAP for the dataset when compared to previous approaches based on CNN and deformed CNN. Furthermore, a deep residual network and fully connected layer-based classification were utilized as the backbone for feature representation. Instance segmentation detects objects by segmenting and classifying regions of interest. The imbalance in the dataset is resolved using a mosaic-tiled approach, which produces greater precision than other approaches evaluated for in the paper.

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Vehicle images dataset for make and model recognition

Cited by 12 publications

References 9 publications

Vehicle make and model recognition using mixed sample data augmentation techniques

Vehicle make and model recognition using mixed sample data augmentation techniques

Vehicle Instance Segmentation Polygonal Dataset for Private Surveillance System

Polygonal Dataset for Vehicle Instance Segmentation for Private Surveillance System

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