Car Type Recognition with Deep Neural Networks
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Abstract (arXiv)
In this paper we study automatic recognition of cars of four types: Bus, Truck, Van and Small car. For this problem we consider two data driven frameworks: a deep neural network and a support vector machine using SIFT features. The accuracy of the methods is validated with a database of over 6500 images, and the resulting prediction accuracy is over 97 %. This clearly exceeds the accuracies of earlier studies that use manually engineered feature extraction pipelines.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Feb 2016.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2021Densely-Populated Traffic Detection using YOLOv5 and Non-Maximum Suppression Ensemblingcited by 6
- 2022DELAUNAY: a dataset of abstract art for psychophysical and machine learning researchcited by 5
- 2022Vehicle and License Plate Recognition with Novel Dataset for Toll Collectioncited by 2
- 2026Explainable Part-Based Vehicle Classifier with Spatial Awarenesscited by 0
- 2026Explainable Part-Based Vehicle Classifier with Spatial Awareness8 May 2026
- 2022Vehicle and License Plate Recognition with Novel Dataset for Toll Collection11 Feb 2022
- 2022DELAUNAY: a dataset of abstract art for psychophysical and machine learning research28 Jan 2022
- 2021Densely-Populated Traffic Detection using YOLOv5 and Non-Maximum Suppression Ensembling27 Aug 2021