RESUMEN
Traditional image classification usually relies on manual feature extraction; however, with the rapid development of artificial intelligence and intelligent vision technology, deep learning models such as CNNs can automatically extract key features from input images to achieve efficient classification. This study focuses on the application of lightweight separable convolutional neural networks in domain-specific image classification tasks. In this paper, we discuss how to use the SSDLite object detection algorithm combined with the MobileNetV2 lightweight convolutional architecture for puppet dynasty recognition from images-a novel and challenging task. By constructing a system that combines object detection and image classification, we aimed to solve the problem of automatic puppet dynasty recognition to reduce manual intervention and improve recognition efficiency and accuracy. We hope that this will have significant implications in the fields of cultural protection and art history research.
RESUMEN
A symmetric all-dielectric metasurface based on silicon and GaAs is proposed and numerically studied. In the mid-infrared region, two Fano resonant peaks with a reflectance exceeding 90% are observed. By altering the geometric parameters of the metasurface, the wavelength location and quality factor (Q-factor) of the resonant peaks can be tuned. The highest Q-factors can be 9609.67 and 3476.33, respectively. The proposed metasurface structure for optical refractive index sensing shows high performance and is insensitive to the plane wave's polarization state. In the refractive index range of 1.00 to 1.10, the highest sensitivity and figure of merit (FoM) are 1901.34 nm RIU-1 and 2492.04 RIU-1, respectively. The highest sensitivity is 2248.57 nm RIU-1 and FoM is 977.64 RIU-1 in the refractive index range of 1.30 to 1.40. These research results will help improve and innovate related sensing technologies and devices.