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A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition.

Sobia Hayee¹, Fawad Hussain¹, Muhammad Haroon Yousaf^1,2

¹Department of Computer Engineering, University of Engineering & Technology, Taxila 47050, Pakistan.

Sensors (Basel, Switzerland)

|September 28, 2023

Summary

This summary is machine-generated.

This study presents a new hybrid CNN model for accurate vehicle make and model recognition (VMMR) despite challenging image conditions and complex classifications. The model effectively extracts fine-grained features for robust VMMR performance.

Keywords:

Fisher discriminative least squares regression VMMR ambiguity hybrid CNN model multiclass classification multiplicity small-scale fine-grained vehicle datasets

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Area of Science:

Computer Vision
Artificial Intelligence
Intelligent Transportation Systems

Background:

Vehicle make and model recognition (VMMR) is crucial for intelligent transportation systems (ITS).
Image challenges like shadows, weather, and occlusions complicate VMMR.
Multiclass classification issues, including multiplicity and ambiguity, hinder accurate VMMR.

Purpose of the Study:

To introduce a novel and robust VMMR model.
To address challenges in VMMR, including image variations and classification complexities.
To achieve accuracy comparable to state-of-the-art methods.

Main Methods:

A hybrid Convolutional Neural Network (CNN) model was developed.
Fisher Discriminative Least Squares Regression (FDLSR) was employed for feature selection.
The deep CNN model was fine-tuned on Stanford-196 and BoxCars21k datasets using ResNet-152 features.

Main Results:

The proposed hybrid CNN model demonstrated high accuracy in VMMR.
Performance improvements of 0.5% and 4% on Stanford-196 compared to SVM and FC layers.
Achieved 0.4% and 1% accuracy gains on BoxCars21k over SVM and FC layers.

Conclusions:

The novel hybrid CNN model offers a robust solution for VMMR.
The model effectively handles image challenges and multiclass classification issues.
This approach is well-suited for small-scale, fine-grained vehicle datasets.