Vehicle Recognition Using CNN

Divyavarshini, V. K.; Govind, Nithyasri; Vasudevan, Amrita; Chamundeeswari, G.; Bharathi, S.

doi:10.1007/978-981-15-5566-4_60

Cited by 2 publications

(1 citation statement)

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“…Divyavarshini et al [14] constituted a sutom CNN model comprising of 25 max pooling layers for vehicle type recignition. Feature extraction is performed by the proposed CNN model and also by the handcrafted HOG feature extractor.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms

et al. 2023

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Vehicle classification is a challenging task in the area of image processing. It involves the classification of various vehicles based on their color, model, and make. A distinctive variety of vehicles belonging to various model categories have been developed in the automobile industry, which has made it necessary to establish a compact system that can classify vehicles within a complex model group. A well-established vehicle classification system has applications in security, vehicle monitoring in traffic cameras, route analysis in autonomous vehicles, and traffic control systems. In this paper, a hybrid model based on the integration of a pre-trained Convolutional Neural Network (CNN) and an evolutionary feature selection model is proposed for vehicle classification. The proposed model performs classification of eight different vehicle categories including sports cars, luxury cars and hybrid power-house SUVs. The used in this work is derived from Stanford car dataset that contains almost 196 cars and vehicle classes. After performing appropriate data preparation and preprocessing steps, feature learning and extraction is carried out using pre-trained VGG16 first that learns and extracts deep features from the set of input images. These features are then taken out of the last fully connected layer of VGG16, and feature optimization phase is carried out using evolution-based nature-inspired optimization model Genetic Algorithm (GA). The classification is performed using numerous SVM kernels where Cubic SVM achieves an accuracy of 99.7% and outperforms other kernels as well as excels in terns of performance as compared to the existing works.

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Section: Related Workmentioning

confidence: 99%