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.
万方数据
维普
