Detection Model of Steel Surface Defect Based on Improved YOLOv7
In the process of steel production,there will be various defects and flaws,which will affect its service life.To address the issue of low efficiency in detecting surface defects during the steel production process,a steel surface defect detection model based on improved YOLOv7 is proposed.The model introduces an improved coupled detection head called the asymmetric multi-level channel compression decoupling head to resolve conflicts between different detection tasks and reduce feature loss in the target confidence task during forward propagation.A lightweight FCSP block is designed to enhance the feature extraction capability of the backbone network and the feature fusion capability of the neck network,improving the model's defect localization ability while significantly increasing detection speed.To further enrich the expression capability of shallow features for small targets,learnable parameters are introduced to promote dynamic feature fusion,facilitating the network in learning diverse features.Experimental results on the NEU-DET dataset demonstrate that the improved model achieves an 8.8 percentage point increase in mAP and an 11.6 FPS improvement compared to the original YOLOv7 model,validating enhancements in both detection accuracy and speed.Generalization experiments on the NEU-DET dataset under reduced lighting conditions and the GC10-DET dataset confirm the model's effectiveness in industrial steel surface defect detection tasks.
万方数据
维普
