A Machine Learning Based Vehicle Classification in Forward Scattering Radar

Kanona, Mohammed E. A.; Alias, Mohamad Y.; Hassan, Mohamed Khalafalla; Mohamed, Khalid S.; Khairi, Mutaz H. H.; Hamdan, Mosab; Hamdalla, Yassin A.; Osman, Omnia M.; Ahmed, Ahmed M. O.

doi:10.1109/access.2022.3183127

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Using WIFI, the vehicles are classified which gain accuracy upto 100%, it has limitation of extracting peak value of the signal. If the signal is weak, there would be a misclassification rate [ 54 , 55 ]. Concerning the features of vehicles like color and shapes from vehicle dataset, the vehicles are classified using CatBoost algorithm.…”

Section: Related Workmentioning

confidence: 99%

Application of an ensemble CatBoost model over complex dataset for vehicle classification

M.,

R. K.,

et al. 2024

PLoS ONE

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The classification of vehicles presents notable challenges within the domain of image processing. Traditional models suffer from inefficiency, prolonged training times for datasets, intricate feature extraction, and variable assignment complexities for classification. Conventional methods applied to categorize vehicles from extensive datasets often lead to errors, misclassifications, and unproductive outcomes. Consequently, leveraging machine learning techniques emerges as a promising solution to tackle these challenges. This study adopts a machine learning approach to alleviate image misclassifications and manage large quantities of vehicle images effectively. Specifically, a contrast enhancement technique is employed in the pre-processing stage to highlight pixel values in vehicle images. In the feature segmentation stage, Mask-R-CNN is utilized to categorize pixels into predefined classes. VGG16 is then employed to extract features from vehicle images, while an autoencoder aids in selecting features by learning non-linear input features and compressing representation features. Finally, the CatBoost (CB) algorithm is implemented for vehicle classification (VC) in diverse critical environments, such as inclement weather, twilight, and instances of vehicle blockage. Extensive experiments are conducted using different large-scale datasets with various machine learning platforms. The findings indicate that CB (presumably a specific method or algorithm) attains the highest level of performance on the large-scale dataset named UFPR-ALPR, with an accuracy rate of 98.89%.

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Section: Related Workmentioning

confidence: 99%

Application of an ensemble CatBoost model over complex dataset for vehicle classification

M.,

R. K.,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The need for reliable target detection and classification drives research that emphasises minimal environmental impact and computational resource reduction. This shift prioritises the use of machine learning applications in the field (Cai et al , 2021; Arab et al , 2021), also considering different signal pre-processing (Kanona et al , 2022) in radar systems. Histogram statistics are becoming a hot topic as they offer a reduction in the computation associated with deep learning algorithms (Shuai et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Analysis of data-driven approaches for radar target classification

Coşkun,

Bilicz

2024

COMPEL

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Purpose This study focuses on the classification of targets with varying shapes using radar cross section (RCS), which is influenced by the target’s shape. This study aims to develop a robust classification method by considering an incident angle with minor random fluctuations and using a physical optics simulation to generate data sets. Design/methodology/approach The approach involves several supervised machine learning and classification methods, including traditional algorithms and a deep neural network classifier. It uses histogram-based definitions of the RCS for feature extraction, with an emphasis on resilience against noise in the RCS data. Data enrichment techniques are incorporated, including the use of noise-impacted histogram data sets. Findings The classification algorithms are extensively evaluated, highlighting their efficacy in feature extraction from RCS histograms. Among the studied algorithms, the K-nearest neighbour is found to be the most accurate of the traditional methods, but it is surpassed in accuracy by a deep learning network classifier. The results demonstrate the robustness of the feature extraction from the RCS histograms, motivated by mm-wave radar applications. Originality/value This study presents a novel approach to target classification that extends beyond traditional methods by integrating deep neural networks and focusing on histogram-based methodologies. It also incorporates data enrichment techniques to enhance the analysis, providing a comprehensive perspective for target detection using RCS.

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A Machine Learning Based Vehicle Classification in Forward Scattering Radar

Cited by 2 publications

References 24 publications

Application of an ensemble CatBoost model over complex dataset for vehicle classification

Application of an ensemble CatBoost model over complex dataset for vehicle classification

Analysis of data-driven approaches for radar target classification

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