用完善的MobileNetV3进行停车场占用检测
Yusufbek Yuldashev1, Mukhriddin Mukhiddinov1, Akmalbek Bobomirzaevich Abdusalomov1,2
1Department of Computer Engineering, Gachon University, Seongnam-si 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究使用优化的MobileNetV3深度学习模型增强了车辆占用检测. 改进的系统准确地识别可用停车位,推进智能停车管理.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 停车场管理系统的研究越来越多,深度学习显示出对占用检测的承诺.
- 准确的车辆占用率确定对于有效的停车管理至关重要.
研究的目的:
- 开发一种先进的深度学习模型,用于在个别停车位精确检测车辆占用率.
- 增强MobileNetV3架构,以提高停车场管理系统的性能.
主要方法:
- 开发了一个优化的MobileNetV3模型,并提供了自定义架构增强.
- 该模型包含一个卷积块注意力机制和蓝图可分离的卷积.
- 在CNRPark-EXT和PKLOT数据集上使用单个停车位补丁进行了培训.
主要成果:
- 增强的MobileNetV3在PKLot数据集上实现了0.99的ROC曲线下的面积 (AUC).
- 该模型在组合数据集上达到98.01%的平均准确性,超过了CarNet (97.03%).
- 与CarNet和mAlexNet等最先进的方法相比,提出的模型显示出更高的性能.
结论:
- 增强的MobileNetV3模型为实时停车位占用检测提供了强大而高效的解决方案.
- 这一进步对改善城市流动性和优化停车资源分配有重大影响.
- 该研究通过改进的停车管理技术,为智能交通系统领域做出了贡献.
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