Improving vehicle classification model algorithm based on ImageNet pretraining

Hongkang Wang

doi:10.1117/12.3021477

19 February 2024 Improving vehicle classification model algorithm based on ImageNet pretraining

Hongkang Wang

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130632W (2024) https://doi.org/10.1117/12.3021477
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

This study employed a training dataset comprising 10 distinct vehicle models for prediction purposes. Among a plethora of models, InceptionV3, MobileNet, and DenseNet were selected due to their relatively high accuracy rates, and they were utilized to enhance the algorithm. Comparative analyses were conducted with the models prior to improvement. The primary approach in this study involved data augmentation, which included rotating images by specific angles, scaling, proportionally shifting images horizontally, and performing horizontal flips on the images within the dataset. Various performance metrics, namely Accuracy, Precision, Recall were calculated using specific formulae. The predictive outcomes for the classification problem were summarized, and specific metric data for different vehicle models were observed to determine the most suitable model for our dataset. A comparative analysis of the predictive accuracy of different network architectures for different types of vehicle models has been determined.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongkang Wang "Improving vehicle classification model algorithm based on ImageNet pretraining", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130632W (19 February 2024); https://doi.org/10.1117/12.3021477

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