用游戏图像训练模型，精准检测真实卡车车轴。
Using Images from a Video Game to Improve the Detection of Truck Axles
- 从游戏提取合成图像训练YOLO模型
- 最高mAP达99%，性能接近真实数据
- 适合缺乏实拍数据的工业检测场景
卷积神经网络（CNN）通常需要大量数据才能达到良好性能，而真实数据采集耗时耗力。生成的合成图像是一种可行替代方案，视频游戏可提供逼真的3D模型。本文研究从游戏提取的图像是否可用于训练CNN以检测真实卡车车轴。构建了三组数据库，包含真实与合成卡车图像，用于训练和测试三种YOLO架构。基于召回率、精确率、F1分数和平均精度均值（mAP）四项指标评估结果，并使用曼-惠特尼U检验分析mAP的统计显著性。结果显示，来自游戏的合成图像可有效提升所有网络的性能，最高mAP达到99%。表明合成图像能作为可靠、低成本的数据源，支持神经网络训练。
原文摘要 · Abstract (English)
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.
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