视觉转换器的分类器检测沟和交通标志
Satish Kumar Satti1, Goluguri N V Rajareddy2, Kaushik Mishra2
1Department of Computer Science and Engineering, VFSTR Deemed to be University, Guntur, 522213, India.
Scientific reports
|January 26, 2024
概括
这项研究引入了一种新的方法,使用级联分类器和视觉变压器来准确地实时检测坑洞和交通标志. 该方法显著改善了识别,这对于自动驾驶汽车和驾驶员安全至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路安全工程 道路安全工程
背景情况:
- 印度的道路交通事故每年造成大约2500人死亡,通常与未被发现的坑洞和交通标志有关.
- 目前的检测方法无法识别具有挑战性的条件,例如充满水,照明或阴影的坑道,以及透视或夜间交通标志.
研究的目的:
- 开发一个强大而准确的实时检测系统,用于坑洞和交通标志.
- 在多样化和具有挑战性的环境条件下克服现有方法的局限性.
主要方法:
- 这是一种新的方法,它结合了用于模式识别的级联分类器和用于详细分析和分类的视觉转换器.
- 在多个数据集上进行培训和评估:ICTS,GTSRDB,KAGGLE和CCSAD.
主要成果:
- 提出的方法实现了高性能指标,包括交通标志检测的平均平均精度 (mAP) 为97.14%和坑洞检测的平均精度为98.27%.
- 在识别精度方面,超越了最先进的技术,如YOLOv3,YOLOv4,更快的RCNN和SSD.
结论:
- 综合级联分类器和视觉转换器方法在检测坑洞和交通标志方面取得了重大进展.
- 这项技术在提高驾驶员辅助系统和自动驾驶汽车的安全性和可靠性方面具有很大的前景.
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