Skip to Main content Skip to Navigation
Conference papers

Classifying logistic vehicles in cities using Deep learning

Abstract : Rapid growth in delivery and freight transportation is increasing in urban areas; as a result the use of delivery trucks and light commercial vehicles is evolving. Major cities can use traffic counting as a tool to monitor the presence of delivery vehicles in order to implement intelligent city planning measures. Classical methods for counting vehicles use mechanical, electromagnetic or pneumatic sensors, but these devices are costly, difficult to implement and only detect the presence of vehicles without giving information about their category, model or trajectory. This paper proposes a Deep Learning tool for classifying vehicles in a given image while considering different categories of logistic vehicles, namely: light-duty, medium-duty and heavy-duty vehicles. The proposed approach yields two main contributions: first we developed an architecture to create an annotated and balanced database of logistic vehicles, reducing manual annotation efforts. Second, we built a classifier that accurately classifies the logistic vehicles passing through a given road. The results of this work are: first, a database of 72 000 images for 4 vehicles classes; and second two retrained convolutional neural networks (InceptionV3 and MobileNetV2) capable of classifying vehicles with accuracies over 90%.
Complete list of metadata

Cited literature [16 references]  Display  Hide  Download
Contributor : SIMON TAMAYO GIRALDO Connect in order to contact the contributor
Submitted on : Friday, May 31, 2019 - 8:17:33 AM
Last modification on : Wednesday, November 17, 2021 - 12:33:05 PM


Classifying logistic vehicles ...
Files produced by the author(s)


  • HAL Id : hal-02144606, version 1
  • ARXIV : 1906.11895


Salma Benslimane, Simon Tamayo, Arnaud de La Fortelle. Classifying logistic vehicles in cities using Deep learning. World Conference on Transport Research, May 2019, Mumbai, India. ⟨hal-02144606⟩



Record views


Files downloads