To accurately extract human body contours,a method for complex background human body contour ex-traction based on the MINet model was proposed.Front-view and side-view human images were captured,and cor-responding mask images were annotated.Diverse complex backgrounds were matched with portraits to create a data-set of 2860 portraits across various scenes.Using a transfer learning mechanism,the MINet salient object detection model was optimized to extract human body contours.The human contour extraction effects of the transfer learn-ing-based MINet model were compared with the original model,the U2Net salient object detection model,the Media-pipe human contour extraction algorithm,and a traditional threshold-based segmentation algorithm.The results show that the transfer learning-based MINet model demonstrates optimal performance in human body contour extrac-tion,with precision,accuracy,recall,and the composite metric F1 reaching 0.998,0.987,0.992,and 0.990,respectively,closely resembling the annotated mask images.This method offers a cost-effective,scalable,and fast approach to extract human body contours from images,providing an effective technique for photo measurement in remote clothing customization.
关键词
人体轮廓提取/MINet模型/人体尺寸测量/远程测体技术
Key words
human body silhouette extraction/MINet model/human body size measurement/remote body measurement technology
维普
万方数据