集装箱病媒生物视觉探寻系统  

Container Vector Visual Search System

作　　者：滕新栋 唐宇豪 马兴录[2] 李晓旭 TENG Xin-Dong;TANG Yu-Hao;MA Xing-Lu;LI Xiao-Xu(Qingdao Customs District P.R.China,Qingdao 266426,China;School of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]中华人民共和国青岛海关,青岛266426 [2]青岛科技大学信息科学技术学院,青岛266061

出　　处：《计算机系统应用》2022年第10期116-121,共6页Computer Systems & Applications

基　　金：国家重点研发计划(2018YFF0214903)

摘　　要：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.

关 键 词：YOLOv5模型 机器视觉 病媒生物 视频传输 深度学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]