集装箱病媒生物视觉探寻系统

Container Vector Visual Search System

针对传统集装箱病媒生物检测局限于人工检测的问题,设计了一款基于机器视觉的集装箱病媒生物视觉探寻系统.系统通过可遥控智能车采集实时视频以及抓取病媒生物的活动,进而通过深度学习和隔帧检测的方法识别遥控车回传视频中的病媒生物.系统以YOLOv5模型为训练核心,采用模块化结构,实现了集装箱病媒生物的视觉检测.利用机器视觉提高了检测效率,为进一步利用机器人检测病媒生物奠定了基础.

To tackle the problem that traditional container vector detection is limited to manual detection,this study designs a visual search system for container vectors based on machine vision.The system collects real-time video and captures the activity of vectors through a smart car under remote control.Then,it recognizes the vectors in the video returned by the car through deep learning and inter-frame detection.The system takes the YOLOv5 model as the training core and adopts a modular structure to realize the visual detection of container vectors.Machine vision helps improve detection efficiency and lays the foundation for the further use of robots to detect vectors.