Aiming at the problems of strong subjectivity and poor background similarity in traditional facial camouflage design,a high fusion facial camouflage design method based on image segmentation technology and deep learning was proposed.Using the OpenCV visual al-gorithm library and FaceMesh deep learning model based on MediaPipe machine learning framework,a high fusion facial camouflage computer-aided design system was constructed.The system can accurately detect and identify facial contours and feature points,extract back-ground features and build a camouflage patch library that conforms to the background charac-teristics,so as to realize the camouflage patches in the face to be automatically called and filled,and to automatically generate facial camouflage design solutions that are highly fused with the background and conform to the facial characteristics.The camouflage effect of the facial camouflage designed by the above method was experimentally verified by establishing a similarity index evaluation system.The results show that this method can effectively improve the scientific nature of facial camouflage design and camouflage effect,and provide a reliable and effective solution for individual soldier to implement facial camouflage quickly and accu-rately on the battlefield.
关键词
迷彩设计/面部迷彩/图像分割/深度学习/背景特征
Key words
camouflage design/facial camouflage/image segmentation/deep learning/back-ground features
维普
万方数据