The reliability of grasp detection for target objects in complex scenes is a challenging task and a critical problem that needs to be solved urgently in practical application. At present, the grasp detection location comes from searching the feature space of the whole image. However, the cluttered background information in the image impairs the accuracy of grasping detection. In this paper, a robotic grasp detection algorithm named MASK-GD is proposed, which provides a feasible solution to this problem. MASK is a segmented image that only contains the pixels of the target object. MASK-GD for grasp detection only uses MASK features rather than the features of the entire image in the scene. It has two stages: the first stage is to provide the MASK of the target object as the input image, and the second stage is a grasp detector based on the MASK feature. Experimental results demonstrate that MASK-GD's performance is comparable with state-of-the-art grasp detection algorithms on Cornell Datasets and Jacquard Dataset. In the meantime, MASK-GD performs much better in complex scenes.

翻译：在复杂场景中,对目标物体进行抓取探测的可靠性是一项具有挑战性的任务,也是在实际应用中需要紧急解决的一个关键问题。目前,抓取探测位置来自搜索整个图像的特征空间。然而,图像中断层的背景资料会损害抓取探测的准确性。在本文中,提出了名为MASK-GD的机器人抓取探测算法,为这一问题提供了可行的解决办法。MASK是一个只包含目标物体像素的分割图像。用于抓取探测的MASK-GD仅使用MASK的特征,而不是整个图像在现场的特征。它有两个阶段:第一阶段是提供目标物体的MASK作为输入图像,第二阶段是基于MASK特征的抓取检测器。实验结果表明MASK-GD的性能与Cnell数据集和Jacqurd数据集的州级抓取测算算算算算算法相当。与此同时,MASK-GD在复杂的场景中表现更好。