基于深度学习的机械手抓取视觉对象识别

Min-Fan Ricky Lee, Fu-Yao Hsu, Hoang-Phuong Doan, Quang-Duy To, Yavier Kristanto
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引用次数: 0

摘要

使用CCD相机进行机械手抓取的视觉对象识别存在不确定性(例如,照明、视点、遮挡和外观)。传统的机器学习分类方法需要在模型学习之前进行明确的特征提取。这些不确定性的存在会影响特征提取的准确性。提出了一种基于深度学习的操作器方法(YOLOv4框架,167层),用于对各种品牌的安全套进行分类。该神经网络结构在PRNet V3和CSPNet的基础上进行了改进,在不影响学习过程中损失函数收敛的前提下减少了计算量。三个主要指标(准确度、精密度和召回率)被用来评估提出的模型的预测。测试场景包括CCD相机与目标之间工作距离和视点的变化。实验结果表明,提出的Yolo v4架构优于其他架构(Yolo v3、Retina Net、ResNet-50和resnet - 10)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning based Visual Object Recognition for Manipulator Grasps
The visual object recognition using a CCD camera for manipulator grasps suffers from uncertainty (e.g., illumination, viewpoint, occlusion, and appearance). The conventional machine learning approach for classification requires a definite feature extraction before the model learning. The accuracy of feature extraction is affected in the presence of those uncertainties. A deep-learning-based approach for a manipulator is proposed (YOLOv4 framework, 167 layers) for the classification of various brands of condoms. This neural network architecture is improved based on the PRNet V3 and CSPNet which reduces computation without affecting the convergence of loss function during the learning. Three primary metrics (accuracy, precision, and recall) are used to evaluate the proposed model's prediction. The testing scenario includes the variation of working distance and viewpoint between the CCD camera and the object. The experiment results show the proposed Yolo v4 outperforms the other architectures (Yolo v3, Retina Net, ResNet-50, and ResNet-l0l).
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