Design of visual rebar detection system based on an improved YOLOv5
Theoretical weight is the mainstream weight calculation method in current steel trade,thus a fast and accurate AI based rebar counting system has become a critical research issue.A design scheme of visual rebar counting system based on cloud-edge collaboration is proposed.The system uses a mobile terminal to capture rebar images,performs preprocessing including background removal and size adjustment,and uploads them to the cloud;The cloud uses a detection model based on an improved YOLOv5 to detect and count the rebars in the image,and feeds back the counting results to the mobile terminal.According to the test results in the SPDC,the counting accuracy of the pro-posed system can reach 99.85％after manual correction,which is higher than the average accuracy of manual count-ing.The time cost of intelligent counting per 1 000 rebars is 26.50 seconds after manual correction,significantly lower than the average time of manual counting.
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