Research on Surface Defect Detection of Hot-Rolled Sheet Based on Improved YOLOv7
In order to improve the speed and accuracy of surface detection of hot-rolled sheet,an improved YOLOv7-BRS object detection algorithm is proposed.Firstly,the ELAN structure in YOLOv7 is improved,and a new computing module BRConv is proposed,which uses deep separable convolution and adds multi-branch hop connection to reduce the complexity of the model,realize model lightweight and improve the detection speed.Secondly,a new attention mechanism for multi-scale recognition is designed,which has dif-ferent receptive fields,which further improves the model's ability to extract important features at different scales.Finally,the loss function is improved,the concept of angle loss is introduced,the penalty index is re-defined,and the convergence speed and accuracy during model training are improved.Experiments show that the improved model volume is reduced by 36%,mAP is increased by 7.3%,and FPS is increased by 14.4 on the NEU-DET dataset.Compared with the current mainstream algorithms,the detection accuracy and speed are significantly improved,and the volume is smaller,which can effectively complete the task of detecting surface defects on the surface of the plate.
defect detectionYOLOv7model lightweightattention mechanismloss function