Deep transfer learning for intelligent vehicle perception: A survey

Liu, Xinyu; Li, Jinlong; Ma, Jin; Sun, Huiming; Xu, Zhigang; Zhang, Tianyun; Yu, Hongkai

doi:10.1016/j.geits.2023.100125

Cited by 10 publications

(4 citation statements)

References 160 publications

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“…In this work, the modal estimation determined the normal frequency of the lateral feature; the outcomes were preserved as information with labels that could be utilized for deep learning. [39] which was used to perform the CAE. [18], [40]- [43].…”

Section: Computer-aided Engineering (Cae) Automationmentioning

confidence: 99%

“…The research undertaken by [39], [53] highlights the application of deep learning neural networks in traffic sign categorization. The study adopts a YOLO-CNN (You Only Look Once Convolutional Neural Network) model, having extra layers compared to a regular neural network.…”

Section: D Vehicle Wheel Under Real World Condition With Deep Learningmentioning

confidence: 99%

“…Using the Intersection over Union (IoU) value-a scale from 0 to 1 that represents the difference between ground truth and projected values-the findings presented in the research showed acceptable performance. This metric, which replaces mean average precision (mAP), [39], was recorded as 0.568, signifying mediocre precision. Farag et al conducted a comparable investigation [98], for autonomous driving, a method based on behavior cloning was used.…”

Section: D Vehicle Wheel Under Real World Condition With Deep Learningmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Akande,

Alabi,

Oyinloye

2024

J. Comput. Theor. Appl.

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Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.

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Section: Computer-aided Engineering (Cae) Automationmentioning

confidence: 99%

Section: D Vehicle Wheel Under Real World Condition With Deep Learningmentioning

confidence: 99%

Section: D Vehicle Wheel Under Real World Condition With Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Akande,

Alabi,

Oyinloye

2024

J. Comput. Theor. Appl.

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show abstract

“…V EHICLE Re-identification (Re-ID) is an important task in intelligent transportation systems [1,2,3,4,5], as it allows for the retrieval of the same vehicle from multiple non-overlapping surveillance cameras. With the availability of vehicle surveillance datasets [6,7,8,9], many vehicle Re-ID models have been proposed [10,11,12,13], which have made significant progress in the past decade and gained wide interest among the research communities of human-machine systems, cybernetics, and transportation.…”

Section: Introductionmentioning

confidence: 99%

Attribute and State Guided Structural Embedding Network for Vehicle Re-Identification

Zheng

et al. 2022

IEEE Trans. on Image Process.

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Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angle leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle Re-ID models in the real world. To promote the Vehicle Re-ID models, this paper proposes to synthesize a large number of vehicle images in the target pose, whose idea is to project the vehicles of diverse poses into the unified target pose so as to enhance feature discrimination. Considering that the paired data of the same vehicles in different traffic surveillance cameras might be not available in the real world, we propose the first Pair-flexible Pose Guided Image Synthesis method for Vehicle Re-ID, named as VehicleGAN in this paper, which works for both supervised and unsupervised settings without the knowledge of geometric 3D models. Because of the feature distribution difference between real and synthetic data, simply training a traditional metric learning based Re-ID model with data-level fusion (i.e., data augmentation) is not satisfactory, therefore we propose a new Joint Metric Learning (JML) via effective feature-level fusion from both real and synthetic data. Intensive experimental results on the public VeRi-776 and VehicleID datasets prove the accuracy and effectiveness of our proposed VehicleGAN and JML.

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Detection of explosives in dustbins using deep transfer learning based multiclass classifiers

Gyasi-Agyei

2024

Appl Intell

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Deep transfer learning for intelligent vehicle perception: A survey

Cited by 10 publications

References 160 publications

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Attribute and State Guided Structural Embedding Network for Vehicle Re-Identification

Detection of explosives in dustbins using deep transfer learning based multiclass classifiers

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