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The S2TLD dataset contains five categories (including red, yellow, green, off, and waiting). It covers a wide range of road scenarios, such as busy street scenes, multiple visible traffic lights, and traffic tail lights that may be confused with traffic lights (e.g., large circular tail lights, etc.). A link to the original dataset: https://github.com/Thinklab-SJTU/S2TLD We randomly divided it into new training set, verification set and test set in a ratio close to 7:2:1.
{"references": ["available at: https://doi.org/10.48550/arXiv.2004.13316"]}
Convolutional Neural Networks, Small Object Detection
Convolutional Neural Networks, Small Object Detection
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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