一种安全高效的机械臂多目标抓取检测方法
A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms
AI总结：
提出一种基于深度学习和边缘-云协作的机械臂多目标抓取方法，兼顾抓取效率与信息安全，通过GAN实现图像加密压缩，在OCID数据集上准确率达92%。
摘要：
机械臂在自动化工业中被广泛使用。然而，随着深度学习在机械臂中的广泛应用，出现了新的挑战，如抓取计算能力的分配和对安全性日益增长的需求。在这项工作中，我们提出了一种基于深度学习和边缘-云协作的机械臂抓取方法。该方法实现了机械臂的任意抓取规划，并兼顾了抓取效率和信息安全。此外，由GAN训练的编码器和解码器使得图像在压缩的同时被加密，从而确保了隐私的安全性。该模型在OCID数据集上达到了92%的准确率，图像压缩比达到0.03%，结构差异值高于0.91。
英文摘要：
Robotic arms are widely used in automatic industries. However, with wide applications of deep learning in robotic arms, there are new challenges such as the allocation of grasping computing power and the growing demand for security. In this work, we propose a robotic arm grasping approach based on deep learning and edge-cloud collaboration. This approach realizes the arbitrary grasp planning of the robot arm and considers the grasp efficiency and information security. In addition, the encoder and decoder trained by GAN enable the images to be encrypted while compressing, which ensures the security of privacy. The model achieves 92% accuracy on the OCID dataset, the image compression ratio reaches 0.03%, and the structural difference value is higher than 0.91.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
