基于深度学习和全景图像的实时停车位检测
Wu Wei1, Hongyang Chen2, Jiayuan Gong1,3,4,5
1School of Intelligent Connected Vehicle, Hubei University of Automotive Technology, Shiyan 442002, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究引入了一种改进的PP-Yoloe算法,用于实时检测停车位,提高自主停车系统的准确性和保持效率. 新型号平衡了智能汽车应用的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 停车位检测对于自主停车系统至关重要.
- 现有的数据集缺乏多样性,无法提供强大的现实世界性能.
- 准确的实时检测仍然是一个重大挑战.
研究的目的:
- 开发一种改进的算法,用于准确和实时检测停车位.
- 使用生成对抗网络 (GAN) 创建一个多样化的全景停车位数据集 (PSEX).
- 为了提高PP-Yoloe的性能,用于检测停车位状态和角度.
主要方法:
- 通过集成基于GAN的图像风格转移来构建PSEX数据集.
- 修改了PP-Yoloe算法,将ResSpp替换为ResSimSppf,在部引入SimSppf,更改激活功能,并添加辅助探测器头.
- 在停车位检测任务上评估了SimSppf_mepre-Yoloe模型.
主要成果:
- 与基线PP-Yoloe相比,SimSppf_mepre-Yoloe模型在mAP50中获得了4.5%的平均改善,在mAP50:95中获得了2.95%的平均改善.
- 该模型保持了相似的推断延迟,在Jetson AGX Xavier上达到33.7 FPS,具有TensorRT优化.
- 基于GAN的数据增强显著丰富了停车位数据的多样性.
结论:
- 拟议的模型在检测准确性和实时性能之间提供了卓越的平衡,用于检测停车位.
- 增强的数据集和算法适用于智能汽车和机器人感知系统的部署.
- 这项研究将自动停车系统的功能推进到完全自主.
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