Research on industrial bar material recognition based on improved YOLOv5
HU Hongsheng
YE Shulin
ZHANG Rendong
Abstract：This paper presents an improved Gs-YOLOv5s model to improve the efficiency of automatic bar feeding in manufacturing industry.By replacing the backbone network of YOLOv5s with the block module of MobileNetV3,the model parameters are reduced and the detection speed is increased.The GSConv module is embedded in the neck network to enrich the output feature map and enhance the model's ability to capture the bar feature,while reducing the number of parameters.Finally,the MPDIoU loss function is used to make the model more accurate in the complex background by introducing distance penalty and area penalty.The experimental results show that the accuracy of Gs-YOLOv5s model is only reduced by 0.2％,but the detection speed reaches 81 frames/s,which is increased by 45.3％,and the number of parameters is reduced by 57.6％.The improved model achieves significant lightweight and detection speed improvement while maintaining accuracy and is suitable for deployment in actual production.
Keywords：bar recognitiontarget detectionYOLOv5sMobileNetV3GSConvMPDIoU
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 43-48 )