Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

中文翻译：

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基于卷积神经网络的单目标抓取检测网络

基于卷积神经网络的抓握检测取得了一定的成果。然而，多层卷积神经网络的过拟合仍然存在，导致检测精度较差。为了获得较高的检测精度，本文提出了一种基于卷积神经网络的广义角度和位置拟合的单目标抓取检测网络。所提出的网络以图像为输入，抓取角度和位置等参数作为输出，采用端到端的检测方式。特别是预处理数据集是为了实现对模型输入的全覆盖，迁移学习是为了避免网络的过拟合。重要的是，一系列实验结果表明，对于单个物体抓取，我们的网络具有良好的检测结果和较高的准确率，