This paper investigates video game identification through single screenshots,
utilizing five convolutional neural network (CNN) architectures (MobileNet,
DenseNet, EfficientNetB0, EfficientNetB2, and EfficientNetB3) across 22 home
console systems, spanning from Atari 2600 to PlayStation 5. Confirming the
hypothesis, CNNs autonomously extract image features, enabling the
identification of game titles from screenshots without additional features.
Using ImageNet pre-trained weights, EfficientNetB3 achieves the highest average
accuracy (74.51%), while DenseNet169 excels in 14 of the 22 systems. Employing
alternative initial weights from another screenshots dataset boosts accuracy
for EfficientNetB2 and EfficientNetB3, with the latter reaching a peak accuracy
of 76.36% and demonstrating reduced convergence epochs from 23.7 to 20.5 on
average. Overall, the combination of optimal architecture and weights attains
77.67% accuracy, primarily led by EfficientNetB3 in 19 systems. These findings
underscore the efficacy of CNNs in video game identification through
screenshots.

通过使用五种卷积神经网络（MobileNet、DenseNet、EfficientNetB0、EfficientNetB2 和 EfficientNetB3），对 22 种家用游戏主机系统（从 Atari 2600 到 PlayStation 5）的单个截图进行视频游戏识别的研究，确认了 CNN 在截图中提取图像特征的能力，实现了在没有额外特征的情况下从截图中识别游戏标题。利用 ImageNet 预训练权重，EfficientNetB3 实现了最高的平均准确性（74.51%），而 DenseNet169 在 22 个系统中的 14 个系统表现出色。使用来自另一个截图数据集的替代初始权重提高了 EfficientNetB2 和 EfficientNetB3 的准确性，后者在平均 23.7 个收敛周期降低到 20.5 个周期，并达到了最高准确性 76.36%。总体而言，通过优化架构和权重的组合，主要由 EfficientNetB3 在 19 个系统中领先，实现了 77.67% 的准确性，这些发现强调了 CNN 在通过截图进行视频游戏识别方面的有效性。