Improved SegFormer network-based line laser segmentation and center extraction method
The extraction of the centerline from multi-line structured light is a critical technique in three-dimensional measurement technologies.Reflectivity and other environmental factors on the surface of the object being measured commonly result in low accuracy and instability in extracting the centerline.This thesis proposes an enhanced laser centerline extraction method.It begins by harnessing global features from line laser images,extracted through the Transformer backbone branch within the encoding layer of the SegFormer network.Additionally,the method integrates the Vgg16 backbone branch to capture shallow contour details from the line laser images.The incorporation of the MSASPP module significantly refines the model's ability to segment linear targets,thus elevating the segmentation accuracy within the laser stripe area.This refined SegFormer network model supplies a superior image source for subsequent centerline extractions,utilizing the Steger method to achieve precise detections.Experimental evidence indicates a 42%enhancement in computational speed over the Steger algorithm,with a notable increase in extraction accuracy by approximately 0.3 pixel.This method proves effective in diverse and complex environments,satisfying industrial demands for precision and stability in inspections.
deep learningline structured lightlight strip center extraction
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