With the increasing number of vehicles, manual security inspections are becoming more laborious at road checkpoints. To address it, a specialized Road Checkpoints Robot (RCRo) system is proposed, incorporated with enhanced You Only Look Once (YOLO) and a 6-degree-of-freedom (DOF) manipulator, for autonomous identity verification and vehicle inspection. The modified YOLO is characterized by large objects’ sensitivity and faster detection speed, named “LF-YOLO”. The better sensitivity of large objects and the faster detection speed are achieved by means of the Dense module-based backbone network connecting two-scale detecting network, for object detection tasks, along with optimized anchor boxes and improved loss function. During the manipulator motion, Octree-aided motion control scheme is adopted for collision-free motion through Robot Operating System (ROS). The proposed LF-YOLO which utilizes continuous optimization strategy and residual technique provides a promising detector design, which has been found to be more effective during actual object detection, in terms of decreased average detection time by 68.25% and 60.60%, and increased average Intersection over Union (IoU) by 20.74% and 6.79% compared to YOLOv3 and YOLOv4 through experiments. The comprehensive functional tests of RCRo system demonstrate the feasibility and competency of the multiple unmanned inspections in practice.

随着车辆数量的增加，人工安检在道路检查站变得越来越费力。为了解决这个问题，提出了一种专门的道路检查站机器人 (RCRo) 系统，该系统与增强的 You Only Look Once (YOLO) 和 6 自由度 (DOF) 机械手相结合，用于自主身份验证和车辆检查。改进后的YOLO具有大物体灵敏度高、检测速度快的特点，命名为“LF-YOLO”。通过基于 Dense 模块的骨干网络连接两尺度检测网络，针对目标检测任务，优化锚框和改进损失函数，实现了对大物体更好的灵敏度和更快的检测速度。在机械手运动过程中，通过机器人操作系统（ROS）采用八叉树辅助运动控制方案进行无碰撞运动。所提出的利用连续优化策略和残差技术的 LF-YOLO 提供了一种有前途的检测器设计，已被发现在实际目标检测中更有效，平均检测时间减少了 68.25% 和 60.60%，并增加了平均交叉点通过实验，与 YOLOv3 和 YOLOv4 相比，over Union (IoU) 分别提高了 20.74% 和 6.79%。RCRo系统的综合功能测试证明了多次无人巡检在实践中的可行性和能力。通过实验发现，在实际目标检测中更有效，平均检测时间减少 68.25% 和 60.60%，平均 Intersection over Union (IoU) 比 YOLOv3 和 YOLOv4 增加 20.74% 和 6.79%。RCRo系统的综合功能测试证明了多次无人巡检在实践中的可行性和能力。通过实验发现，在实际目标检测中更有效，平均检测时间减少 68.25% 和 60.60%，平均 Intersection over Union (IoU) 比 YOLOv3 和 YOLOv4 增加 20.74% 和 6.79%。RCRo系统的综合功能测试证明了多次无人巡检在实践中的可行性和能力。