基于深度学习的路面坑洼检测系统设计  

Design of Pavement Pothole Detection System Based on Deep Learning

作　　者：焦双健[1] 杜福君 Jiao Shuangjian;Du Fujun(Faculty of Engineering,Ocean University of China,Qingdao 266100,China)

机构地区：[1]中国海洋大学工程学院,青岛266100

出　　处：《单片机与嵌入式系统应用》2022年第7期10-13,共4页Microcontrollers & Embedded Systems

摘　　要：提出一种基于深度学习网络的路面坑洼嵌入式检测系统的设计方案,旨在提升路面养护巡检的效率、降低公路维护费用。该系统首先对大量的样本数据进行网络模型训练,获取最优模型;然后将最优模型部署到英伟达TX2中;最后通过车载摄像头自动检测路面坑洼,并将坑洼信息报送给路面养护部门,实现公路坑洼的自动化巡检。In the paper,the design scheme of a pavement pothole embedded detection system based on deep learning network is proposed,which aims to improve the efficiency of road surface maintenance inspection and reduce highway maintenance costs.The system first trains the network model through a large number of sample data,obtains the optimal model,and then deploys the optimal model to NVIDIA TX2,and finally automatically detects the road potholes through the on-board camera,and submits the road pothole information to the road maintenance department to realize the automatic inspection of the road potholes.

关 键 词：路面坑洼检测 英伟达TX2 深度学习 YOLOv5S 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]