Identification of pattern elements in men's fashion design sketches based on deep learning
The application of deep learning technology in the traditional field of fashion pattern making is ex-plored,aiming to replace the manual cognitive process of pattern makers in extracting key dimension points and component category information from design sketches.This approach seeks to improve the efficiency and quality of fashion structural design.Firstly,the constituent elements of classic fashion design sketches are analyzed,and the cognitive patterns of pat-tern makers are investigated when extracting key information from these sketches.Secondly,the design and implementation process of a design sketch recognition model based on Hourglass and YOLOv5 is introduced.A dataset comprising 15 key measurement points for garments and another dataset containing 15 common men's clothing components are preprocessed,annotated,and augmented,covering a total of 2 000 men's fashion design sketches.These datasets are used to train multi-ple models iteratively,and the performance metrics of these models are tested,evaluated,and analyzed.Deep learning tech-nology successfully achieved the automatic extraction of key information from design sketches,effectively replacing the tra-ditional manual interpretation process of pattern makers,thereby improving design efficiency and quality.Future research directions include expanding the dataset scale,enriching label categories,exploring more complex model structures,and considering applications in other fields such as women's and children's fashion design sketch recognition.
deep learningdesign sketch information recognitionpattern making process optimizationHourglassYOLOv5
万方数据
维普
