Car detection and classification using cascade model

Liang, Jun; Chen, Xu; He, Meiling; Chen, Long Cong; Cai, Tao; Zhu, Ning

doi:10.1049/iet-its.2018.5270

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…Liang et al's data augmentation and pre-training of a CNN using raw images can classify vehicle brands and models with an accuracy rate close to 80%. 3 Classic fault detection methods based on statistical residual evaluation are difficult to detect small deviation faults. Tran et al developed a prediction model using support vector regression to obtain a fault-free reference.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the large difference in car appearance in images, capturing key information about car pose is critical. Liang et al's data augmentation and pre‐training of a CNN using raw images can classify vehicle brands and models with an accuracy rate close to 80% 3 . Classic fault detection methods based on statistical residual evaluation are difficult to detect small deviation faults.…”

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

View full text Add to dashboard Cite

show abstract

“…Image recognition has long been known to be a very difficult problem to solve for computers and computer systems, but humans can perform these tasks with ease. Such noteworthy results achieved by CNNs have been one of the reasons for their widespread use in all sorts of tasks from number plate detection [2] to car classification [3].…”

Section: Introductionmentioning

confidence: 99%