针对非结构化环境中多目标抓取检测存在速度慢、效果差的问题,提出一种先目标检测后抓取检测的方法.在目标检测中,为了加快网络的运行速度,文中采用深度可分离卷积和坐标注意力机制对YOLOv5网络进行轻量化改进.在抓取任务中,设计了一种单阶段抓取位姿检测算法.首先,考虑到非结构化环境中存在的干扰,选用RGB-D图像作为抓取网络的输入数据,并选用GG-CNN作为主干网络;其次,为了加强抓取网络的特征提取能力,利用Inception-ResNet模块中不同大小卷积核的并联使用来拓宽网络感受野,同时无参三维注意力机制的融入使得网络更专注于抓取信息特征,抑制背景噪声信息;最后,使用抓取质量评估来对抓取框进行修正,并输出置信度和最高的抓取框.实验结果表明:轻量化的目标检测网络参数量为2 776 708,每秒帧数(frames per second,FPS)为102;改进后的抓取检测网络在公共Cornell数据集上,取得了 96.57％的准确率,FPS为54.17.这说明2个网络能部署在机械臂上并较好地实现在多目标场景下的抓取任务,可以应用到实际工业生产中.
Deep learning-based object detection and robotic arm grasping
Addressing the issues of slow speed and poor performance in multi-object grasping detection in unstructured environments,a method of performing object detection before grasping detection is pro-posed.In object detection,to accelerate the network's running speed,this paper improved the YOLOv5 network by employing depth wise separable convolutions and coordinate attention mechanisms.For the grasping task,a single-stage grasping pose detection algorithm was designed.Firstly,considering the in-terference present in unstructured environments,RGB-D images were selected as the input data for the grasping network,and GG-CNN was chosen as the backbone network.Secondly,to enhance the feature extraction capabilities of the grasping network,the parallel use of different-size convolutional kernels in the Inception-ResNet module was utilized to broaden the network's receptive field.Additionally,the inte-gration of a parameter-free three-dimensional attention mechanism enabled the network to focus more on grasping information features and suppress background noise.Finally,a grasping quality evaluation was employed to refine the grasping boxes,and the grasping box with the highest confidence score was out-put.The experimental results indicate that the improved object detection network has a parameter count of 2 776 708 and achieves 102 frames per second(FPS).On the public Cornell dataset,the improved grasping detection network achieves an accuracy of 96.57％with a FPS of 54.17.The combination of the two improved networks can be deployed on robotic arms and effectively accomplish grasping tasks in multi-object scenarios,making them suitable for practical industrial applications.
万方数据
维普
