YOLOX-αSMV Algorithm for Surface Defect Detection of Strip Steel Material
[Objective]In order to solve the problems of insufficient feature extraction and weak ability of multi-target defect detection of YOLOX algorithm in steel surface defect detection,a multi-dimensional feature fusion strip material surface defect detection algorithm based on improved loss function is proposed.[Method]First of all,apply SPP_SF to the Backbone part to retain multi-scale feature information and improve classification accu-racy.Secondly,the multi-dimensional feature fusion module MDFFM is added in the Neck part to integrate the channel,space and position information into the feature vector to strengthen the feature ex-traction ability of the algorithm.Finally,the introduction of Varifocal Loss and α-CIoU is weighted with positive and negative samples to improve the regression accuracy of the prediction box.[Result]The experimental results show that YOLOX-αSMV in NEU-DET data set mAP@0.5:0.95 reaches 47.54%,which is 3.43%higher than YOLOX algorithm.[Conclusion]The algorithm significantly improves the recognition and localization of fuzzy defects and small target defects while keeping the detection speed basically unchanged.
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