Aiming at the problems of many types of defects on the surface of steel plate,large defect differences,high leakage detection rate,etc.,a defect detection algorithm to improve YOLOv9 is proposed.Firstly,the algorithm improves the RepNCSPELAN4 module in the feature extraction network through the FasterBlock in FasterNet,and the RepNCSPELAN4-FB module is designed to realize the multi-scale feature fusion,so as to reduce the number of parameters of the model,and secondly,using the inverse residual structure of iRMB and a kind of highly efficient multi-scale attention module,EMAttention,to combine to form a new iEMA module that improve the accuracy of the network,and finally,using the Inner-WIOU loss function to improve the bounding box regression loss,which improves the model's detection performance for inhomogeneous distributions and target defects at different scales.Through experiments on the GC10-DET dataset,the improved algorithm improves the precision,recall and map@0.5 by 3.5%、3%and 2.1%compared with the original algorithm.The model shows good performance in steel surface defect detection.
维普
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