Intelligent extraction method of image marker point features based on improved YOLOv5 model
Marker points are widely used in fields such as photogrammetry and computer vision,where the extrac-tion of image marker points is a key step for further applications in later stages.Therefore,this paper proposes an intel-ligent extraction method of image marker point features based on the improved YOLOv5 model,which has better adapt-ability and extraction efficiency compared with traditional algorithms.First,a method is proposed to construct a marker point sample library based on the limit sample condition,which can rapidly and automatically expand the marker point samples.Then,according to the small target characteristics of marker points,the spatial and semantic features in YOLOv5 network are fused,and the coordinate attention mechanism is added to improve the feature extraction ability of deep learning network for marker points.The experimental results show that the correct rate of marker point extraction by the method in this paper reaches 96％,and the average extraction time for each image is 0.073 s.This method can provide new ideas and methods for the intelligent extraction of marker points in practical engineering.
deep learningextreme samplesimage marker pointsintelligent extractionYOLOv5
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