Deep learning-based algorithm for vehicle detection in intelligent transportation systems

Qiu, Linrun; Zhang, Dongbo; Tian, Yuan; Al-Nabhan, Najla

doi:10.1007/s11227-021-03712-9

Cited by 29 publications

(17 citation statements)

References 25 publications

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“…The majority of papers reviewed focused on improving the accuracy of VDC systems. Currently, the state‐of‐the‐art VDC systems obtained around 97% accuracy and 99% precision on some datasets 20,74,75,77 …”

Section: Discussionmentioning

confidence: 99%

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A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Hashemi

Emami

Sangar

2021

Int J Communication

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Summary The vehicle detection and classification (VDC) problem has received much attention recently due to the increased security threats and the need to develop intelligent transportation systems. A large number of approaches have been proposed for the VDC problem using neural networks. To determine how neural networks‐based approaches have developed for the VDC in recent years, this paper surveys the VDC approaches through a literature review with the range Jan. 2012 through Apr. 2021. To do this, we introduce a new comparison framework to classify and compare the VDC approaches. Our proposed framework is composed of nine comparison dimensions: input data type, vehicle type, scale, scope, dynamicity, vehicle detection method, vehicle classification method, application, and evaluation method. Next, using the proposed framework, we discuss the evolution of the VDC approaches and identify several open issues that have emerged in the field. This paper provides a guide for researchers to use or design robust VDC systems with proper characteristics based on their needs.

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Section: Discussionmentioning

confidence: 99%

“…There are various approaches to perform vehicle classification including rule‐based methods, 15 machine‐learning methods, 2,36,42,68–80 data mining methods, 73 soft computing methods, 7 and specific methods 5,26 . A summary of the vehicle classification methods is shown in Figure 9.…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Hashemi

Emami

Sangar

2021

Int J Communication

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“…Using Adaboost 85.8% accuracy is achieved [3]. Qiu et al [5] compared the performance of haar features along with convolution neural network (CNN). Using haar-like  ISSN: 2252-8938 Int J Artif Intell, Vol.…”

Section: Introductionmentioning

confidence: 99%

“…12, No. 1, March 2023: 137-145 138 features, 86.72% and 91.86% precision and recall are achieved, which increased by 5.63% and 0.2% with CNN [5]. Gholamalinejad and Khosravi proposed a novel CNN architecture composed of CNN layers with squeeze-and-excitation (SE) modules.…”

Section: Introductionmentioning

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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“…Aiming at the vehicle detection method based on sensors, this method is simple to operate and does not need complex procedures, but the environment adaptability is poor [9]. e combination of HOG features and support vector machine provides a new idea for construction vehicle identi cation: rst, the extracted image is preprocessed, and then the target area is extracted according to the shape, color, and other characteristics of the construction vehicle, which reduces the target detection range e ectively [10].…”

Section: Introductionmentioning

confidence: 99%

Combining Self-Supervised Learning and Yolo v4 Network for Construction Vehicle Detection

Zhang

Hou

2022

Mobile Information Systems

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At present, there are many application fields of target detection, but it is very difficult to apply intelligent traffic target detection in the construction site because of the complex environment and many kinds of engineering vehicles. A method based on self-supervised learning combined with the Yolo (you only look once) v4 network defined as “SSL-Yolo v4” (self-supervised learning-Yolo v4) is proposed for the detection of construction vehicles. Based on the combination of self-supervised learning network and Yolo v4 algorithm network, a self-supervised learning method based on context rotation is introduced. By using this method, the problem that a large number of manual data annotations are needed in the training of existing deep learning algorithms is solved. Furthermore, the self-supervised learning network after training is combined with Yolo v4 network to improve the prediction ability, robustness, and detection accuracy of the model. The performance of the proposed model is optimized by performing five-fold cross validation on the self-built dataset, and the effectiveness of the algorithm is verified. The simulation results show that the average detection accuracy of the SSL-Yolo v4 method combined with self-supervised learning is 92.91%, 4.83% detection speed is improved, 7–8 fps detection speed is improved, and 8–9% recall rate is improved. The results show that the method has higher precision and speed and improves the ability of target prediction and the robustness of engineering vehicle detection.

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Deep learning-based algorithm for vehicle detection in intelligent transportation systems

Cited by 29 publications

References 25 publications

A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Vehicle make and model recognition using mixed sample data augmentation techniques

Combining Self-Supervised Learning and Yolo v4 Network for Construction Vehicle Detection

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