Manipulator-based autonomous inspections at road checkpoints: Application of faster YOLO for detecting large objects

Abstract

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.