@inproceedings{12584,
  keywords = {Convolution neural network, Convolutional neural networks, Deep neural networks, Detection methods, Digital image, Digital protection, Edge structures, Image segmentation, Image texture, Inner convolution neural network, Intangible cultural heritages, Long embroidery, Yolov8},
  author = {Yuanxiao Ba},
  title = {Research on GSEm-Net detection method of embroidery pattern lightweight operator for digital protection of Gansu intangible cultural heritage Long Embroidery},
  abstract = {The digital image of embroidery has complex edge structure and high repetition rate texture features. Post-processing such as region detection is a challenging task. A long embroidery image data set for learning and training is constructed. Based on the deep network and multi-scale detection structure, the lightweight detection network model GSEm-Net is introduced to detect the long embroidery pattern in real time. Experiments show that compared with yoloV8, the detection accuracy of this method is equivalent, and the detection speed is 1.5 times that of yoloV8, but the generated model is smaller, and it is easier to deploy to low-computing edge devices, which effectively improves the target detection efficiency.},
  booktitle = {Proceedings of SPIE - The International Society for Optical Engineering},
  volume = {13550},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001143728&doi=10.1117%2f12.3059274&partnerID=40&md5=215645e0cdb65d1c4fe653b28da0bbe2},
  doi = {10.1117/12.3059274},
  note = {Type: Conference paper},
}