With the widespread adoption and development of mobile devices, vision-based recognition applications have become a hot topic in research. Jade, as an important cultural heritage and artistic item, has significant applications in fields such as jewelry identification and cultural relic preservation. However, existing jade recognition systems still face challenges in mobile implementation, such as limited computing resources, real-time requirements, and accuracy issues. To address these challenges, this paper proposes a jade recognition system based on size model collaboration, aiming to achieve efficient and accurate jade identification using mobile devices such as smartphones.First, we design a size model based on multi-scale image processing, extracting key visual information by analyzing jade's dimensions, shapes, and surface textures. Then, a collaborative multi-model classification framework is built by combining deep learning and traditional computer vision algorithms. This framework can effectively select and adjust models based on different jade characteristics, providing high accuracy results across various environments and devices.Experimental results show that the proposed system can provide high recognition accuracy and fast processing time on mobile devices, while consuming relatively low computational resources. The system not only holds great application potential but also provides new ideas and technical support for the intelligent development of jade identification.

翻译：随着移动设备的广泛应用与发展，基于视觉的识别应用已成为研究热点。玉石作为重要的文化遗产与艺术品，在珠宝鉴定、文物保护等领域具有重要应用价值。然而，现有玉石识别系统在移动端实现仍面临计算资源有限、实时性要求高以及准确度不足等挑战。为解决这些问题，本文提出一种基于尺寸模型协作的玉石识别系统，旨在利用智能手机等移动设备实现高效精准的玉石识别。首先，我们设计了基于多尺度图像处理的尺寸模型，通过分析玉石的尺寸、形状及表面纹理提取关键视觉信息。随后，通过结合深度学习与传统计算机视觉算法，构建了协作多模型分类框架。该框架能够根据不同玉石特征有效选择与调整模型，在不同环境与设备上提供高精度识别结果。实验结果表明，所提系统在移动设备上能够实现高识别准确率与快速处理速度，同时消耗相对较低的计算资源。该系统不仅具有广阔的应用前景，也为玉石鉴定的智能化发展提供了新思路与技术支撑。