Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Truck Axle Detection with Convolutional Neural Networks   Axle count in trucks is important to the classification of vehicles and to
the operation of road systems, and is used in the determination of service fees
and 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. Training was done on different
base models, to increase training time efficiency and to compare results. We
evaluated results based on three metrics: 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. Dataset and codes are publicly available for
download.

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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
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The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga