  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%.
