基于YOLOv5的货物识别系统

Goods Recognition System Based on YOLOv5

针对仓储物流中人工检测货物范围受限且效率较低的问题,研发了一种基于YOLOv5目标检测的货物识别系统,旨在快速、准确地识别货物种类及破损情况并及时反馈。建立数据集并加以训练,得到用于YOLOv5检测货物的模型,在此基础上,设计可交互式界面,结合摄像头实时检测,并根据检测结果实时反馈。试验结果表明,在光照充足的情况下,程序可以较好地完成货物检测并报警的任务。

In response to the limited range and low efficiency of manual goods inspection in warehousing logistics,a goods recognition system based on YOLOv5 object detection was developed.The system aims to rapidly and accurately identify the types and damage conditions of goods,providing timely feedback upon detecting damaged goods.A dataset was established and trained to obtain a model for YOLOv5 goods detection.Subsequently,an interactive interface was designed to integrate real-time detection using a camera and provide feedback based on the detection results.Experimental results indicate that under sufficient lighting conditions,the program can effectively complete the task of goods detection and alerting.