Summary of Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games, by Carlos Garcia Ling et al.
Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games
by Carlos Garcia Ling, Konrad Tollmar, Linus Gisslen
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach for detecting common glitches in video games using Deep Convolutional Neural Networks (DCNNs). By training a ShuffleNetV2 model on generated data, researchers achieved an accuracy of 86.8% and a false positive rate of 8.7%. The model is capable of generalizing to detect unseen graphical anomalies, demonstrating effective performance in detecting glitches across different textures. The work also proposes a confidence measure and aggregation technique for improved detection in production settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a way to use computers to find problems in video games. They used special computer programs called Deep Convolutional Neural Networks (DCNNs) to look at pictures of game glitches and decide which ones are real or not. The computer was very good at finding the glitches, with a success rate of about 87%. The computer can even work on new things it hasn’t seen before, like different textures in the game. This could help make video games better by automatically checking for problems. |
