Xie Xiaona, Liu Zeqian, Wang Yuanshuai, Fu Haoyue, Liu Mengqi, Zhang Yingqin, Xu Jinbo
Art College, Northeastern University, No. 11 Lane, Wenhua Road, Heping District, Shenyang 110819, China.
College of Sciences, Northeastern University, No. 11 Lane, Wenhua Road, Heping District, Shenyang 110819, China.
Entropy (Basel). 2024 Jul 29;26(8):645. doi: 10.3390/e26080645.
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.
传统的图像分类通常依赖于手动特征提取；然而，随着人工智能和智能视觉技术的快速发展，诸如卷积神经网络（CNNs）等深度学习模型能够从输入图像中自动提取关键特征以实现高效分类。本研究聚焦于轻量级可分离卷积神经网络在特定领域图像分类任务中的应用。在本文中，我们讨论了如何使用SSDLite目标检测算法结合MobileNetV2轻量级卷积架构从图像中识别傀儡王朝——这是一项新颖且具有挑战性的任务。通过构建一个将目标检测与图像分类相结合的系统，我们旨在解决傀儡王朝自动识别问题，以减少人工干预并提高识别效率和准确性。我们希望这将在文化保护和艺术史研究领域产生重大影响。
