This paper aims at providing researchers and engineering professionals from the first step of solution development to the last step of solution deployment with a practical and comprehensive deep‐learning‐based solution for detecting construction vehicles. This paper places particular focus on the often‐ignored last step of deployment. Our first phase of solution development involved data preparation, model selection, model training, and model validation. Given the necessarily small‐scale nature of construction vehicle image datasets, we propose as detection model an improved version of the single shot detector MobileNet, which is suitable for embedded devices. Our study's second phase comprised model optimization, application‐specific embedded system selection, economic analysis, and field implementation. Several embedded devices were proposed and compared. Results including a consistent above 90% mean average precision confirm the superior real‐time performance of our proposed solutions. Finally, the practical field implementation of our proposed solutions was investigated. This study validates the practicality of deep‐learning‐based object detection solutions for construction scenarios. Moreover, the detailed information provided by the current study can be employed for several purposes such as safety monitoring, productivity assessments, and managerial decision making.

中文翻译：

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基于深度学习的计算机视觉解决方案，用于工程车辆检测

本文旨在为从解决方案开发的第一步到解决方案部署的最后一步的研究人员和工程专业人士提供实用，全面的基于深度学习的解决方案，以检测建筑车辆。本文特别关注通常被忽略的部署的最后一步。解决方案开发的第一阶段涉及数据准备，模型选择，模型训练和模型验证。考虑到建筑车辆图像数据集的必然小规模性质，我们提出了一种适用于嵌入式设备的单发检测器MobileNet的改进版本作为检测模型。我们研究的第二阶段包括模型优化，特定于应用程序的嵌入式系统选择，经济分析和现场实施。提出并比较了几种嵌入式设备。结果包括平均平均精度始终高于90％，这证明了我们提出的解决方案具有出色的实时性能。最后，研究了我们提出的解决方案的实际现场实施情况。这项研究验证了基于深度学习的对象检测解决方案在施工方案中的实用性。此外，当前研究提供的详细信息可用于多种目的，例如安全监控，生产率评估和管理决策。这项研究验证了基于深度学习的对象检测解决方案在施工方案中的实用性。此外，当前研究提供的详细信息可用于多种目的，例如安全监控，生产率评估和管理决策。这项研究验证了基于深度学习的对象检测解决方案在施工方案中的实用性。此外，当前研究提供的详细信息可用于多种目的，例如安全监控，生产率评估和管理决策。