Car detection and damage segmentation in the real scene using a deep learning approach

Parhizkar, Mahboub; Amirfakhrian, Majid

doi:10.1007/s41315-022-00231-5

Cited by 10 publications

(2 citation statements)

References 42 publications

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“…Parhizkar and Amirfakhrian used convolutional neural network (CNN)'s method for automatic car and damage detection, using two different CNNs, to determine damage in the areas outside the car in the acquired images. 1 You et al proposed fault detection and isolation technology for vehicle yaw moment control system. Through a simulation study of a real vehicle dynamics model, the proposed algorithm can isolate the components affected by the fault.…”

Section: Related Workmentioning

confidence: 99%

“…Automatic detection of damage on car exterior surfaces can reduce costs, and the use of visual inspection can be a huge help in this effort. Parhizkar and Amirfakhrian used convolutional neural network (CNN)'s method for automatic car and damage detection, using two different CNNs, to determine damage in the areas outside the car in the acquired images 1 . You et al proposed fault detection and isolation technology for vehicle yaw moment control system.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic management network system of automobile detection applying edge computing

Cao

2023

Int J Network Mgmt

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Dynamic vehicle detection requires the transmission of large amounts of data collected by different types of sensors to the edge computing nodes. This is likely to cause network delays and congestion, affecting the computation of the edge computing nodes and thus posing serious security risks. Therefore, optimizing data transmission between vehicles and edge computing nodes is a new challenge to be addressed in the practical application of edge computing‐based vehicle dynamic detection architectures. The data requirements of VDT for vehicle detection dynamic detection in different environments are considered, the optimization objectives and constraints are analysed, and a deviation detection and greedy algorithm is proposed in this paper to address the problems of long mixed‐integer linear programme solution time and insufficient practical applications, and the performance of the algorithm is evaluated through simulation experiments conducted by simulation of urban mobility, a traffic flow simulation tool, and PreScan, a vehicle simulation test software. The results show that compared with the deviation detection algorithm, the greedy algorithm can reduce the communication overhead by 82.6%–86.2% in all cases and improve the performance by 13.6%–19.5%, which is more suitable for practical applications. The results of this paper contribute to the automation and modernization of vehicle technology management and information transfer.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dynamic management network system of automobile detection applying edge computing

Cao

2023

Int J Network Mgmt

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MARS: Mask Attention Refinement with Sequential Quadtree Nodes for Car Damage Instance Segmentation

Panboonyuen,

Nithisopa,

Pienroj

et al. 2024

Lecture Notes in Computer Science

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Object tracking in infrared images using a deep learning model and a target-attention mechanism

Parhizkar

Karamali

Ravan

2022

Complex Intell. Syst.

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Small object tracking in infrared images is widely utilized in various fields, such as video surveillance, infrared guidance, and unmanned aerial vehicle monitoring. The existing small target detection strategies in infrared images suffer from submerging the target in heavy cluttered infrared (IR) maritime images. To overcome this issue, we use the original image and the corresponding encoded image to apply our model. We use the local directional number patterns algorithm to encode the original image to represent more unique details. Our model is able to learn more informative and unique features from the original and encoded image for visual tracking. In this study, we explore the best convolutional filters to obtain the best possible visual tracking results by finding those inactive to the backgrounds while active in the target region. To this end, the attention mechanism for the feature extracting framework is investigated comprising a scale-sensitive feature generation component and a discriminative feature generation module based on the gradients of regression and scoring losses. Comprehensive experiments have demonstrated that our pipeline obtains competitive results compared to recently published papers.

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Car detection and damage segmentation in the real scene using a deep learning approach

Cited by 10 publications

References 42 publications

Dynamic management network system of automobile detection applying edge computing

Dynamic management network system of automobile detection applying edge computing

MARS: Mask Attention Refinement with Sequential Quadtree Nodes for Car Damage Instance Segmentation

Object tracking in infrared images using a deep learning model and a target-attention mechanism

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