Using Images from a Video Game to Improve the Detection of Truck Axles

Convolutional Neural Networks (CNNs) traditionally require large amounts of data to train models with good performance. However, data collection is an expensive process, both in time and resources. Generated synthetic images are a good alternative, with video games producing realistic 3D models. This paper aims to determine whether images extracted from a video game can be effectively used to train a CNN to detect real-life truck axles. Three different databases were created, with real-life and synthetic trucks, to provide training and testing examples for three different You Only Look Once (YOLO) architectures. Results were evaluated based on four metrics: recall, precision, F1-score, and mean Average Precision (mAP). To evaluate the statistical significance of the results, the Mann-Whitney U test was also applied to the resulting mAP of all models. Synthetic images from trucks extracted from a video game proved to be a reliable source of training data, contributing to the performance of all networks. The highest mAP score reached 99%. Results indicate that synthetic images can be used to train neural networks, providing a reliable, low-cost data source for extracting knowledge.