  Axle count in trucks is important to the classification of vehicles and to
the operation of road systems. It is used in the determination of service fees
and in the impact on the pavement. Although axle count can be achieved with
traditional methods, such as manual labor, it is increasingly possible to count
axles using deep learning and computer vision methods. This paper aims to
compare three deep-learning object detection algorithms, YOLO, Faster R-CNN,
and SSD, for the detection of truck axles. A dataset was built to provide
training and testing examples for the neural networks. The training was done on
different base models, to increase training time efficiency and to compare
results. We evaluated results based on five metrics: precision, recall, mAP,
F1-score, and FPS count. Results indicate that YOLO and SSD have similar
accuracy and performance, with more than 96\% mAP for both models. Datasets and
codes are publicly available for download.
