基于改进的MMAL-Net的自动超载检测系统的卡车型号识别
Jiachen Sun1, Jin Su2, Zhenhao Yan1
1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
Frontiers in neuroscience
|August 28, 2023
概括
本研究介绍了一种新的卡车超载检测方法,使用改进的MMAL-Net进行卡车模型识别. 该系统通过在称重站准确识别过载的卡车来提高道路安全和基础设施.
科学领域:
- 计算机视觉 计算机视觉
- 后勤和运输工程 后勤和运输工程
- 道路安全工程 道路安全工程
背景情况:
- 卡车超载严重损害了道路基础设施的完整性和交通安全.
- 准确检测和预防卡车超载对于可持续物流和公共安全至关重要.
- 现有的方法往往缺乏实时,可靠的超载车辆识别所需的精度.
研究的目的:
- 通过先进的图像识别技术,引入一种用于检测卡车超载的新方法.
- 为了提高确定超载评估卡车模型的准确性和效率.
- 将卡车模型识别与称重站数据集成为实时超载警报.
主要方法:
- 利用改进的MMAL-Net (多模态注意力学习网络) 来从正面和侧面图像识别卡车模型.
- 应用APPM (基于注意力的部件建议模块) 用于在侧卡车图像中进行本地细分和部件识别.
- 集成识别系统与自动称重站数据和车牌信息.
主要成果:
- 改进的MMAL-Net在斯坦福汽车基准数据集上实现了95.03%的准确性.
- 在小规模数据集上,识别准确度在20个训练样本中达到85%,在50个训练样本中达到100%.
- 集成系统可通过组合模型识别,重量数据和车牌来实时评估卡车超载情况.
结论:
- 拟议的方法为检测卡车超载提供了高度准确和可靠的解决方案.
- 与现有基础设施 (称重站) 的整合有助于加强道路安全的实际实施.
- 这项技术对于减轻卡车超载对基础设施和公共安全的负面影响至关重要.
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