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.

中文翻译：

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基于Mask-GD分割的机器人抓取检测

复杂场景下目标物体抓握检测的可靠性是一项艰巨的任务，也是一个在实际应用中亟待解决的关键问题。目前，抓握检测位置来自搜索整个图像的特征空间。但是，图像中混乱的背景信息会损害抓取检测的准确性。提出了一种名为MASK-GD的机器人抓握检测算法，为该问题提供了一种可行的解决方案。MASK是仅包含目标对象像素的分割图像。用于抓握检测的MASK-GD仅使用MASK功能，而不使用场景中整个图像的功能。它分为两个阶段：第一个阶段是提供目标对象的遮罩作为输入图像，第二阶段是基于MASK功能的抓握检测器。实验结果表明，MASK-GD的性能可与Cornell数据集和Jacquard数据集上的最新抓握检测算法相媲美。同时，MASK-GD在复杂场景中的表现要好得多。