Bai Jimeng, Cao Guohua
School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel). 2024 Dec 20;24(24):8141. doi: 10.3390/s24248141.
In industrial applications, robotic arm grasp detection tasks frequently suffer from inadequate accuracy and success rates, which result in reduced operational efficiency. Although existing methods have achieved some success, limitations remain in terms of detection accuracy, real-time performance, and generalization ability. To address these challenges, this paper proposes an enhanced grasp detection model, G-RCenterNet, based on the CenterNet framework. First, a channel and spatial attention mechanism is introduced to improve the network's capability to extract target features, significantly enhancing grasp detection performance in complex backgrounds. Second, an efficient attention module search strategy is proposed to replace traditional fully connected layer structures, which not only increases detection accuracy but also reduces computational overhead. Additionally, the GSConv module is incorporated during the prediction decoding phase to accelerate inference speed while maintaining high accuracy, further improving real-time performance. Finally, ResNet50 is selected as the backbone network, and a custom loss function is designed specifically for grasp detection tasks, which significantly enhances the model's ability to predict feasible grasp boxes. The proposed G-RCenterNet algorithm is embedded into a robotic grasping system, where a structured light depth camera captures target images, and the grasp detection network predicts the optimal grasp box. Experimental results based on the Cornell Grasp Dataset and real-world scenarios demonstrate that the G-RCenterNet model performs robustly in grasp detection tasks, achieving accurate and efficient target grasp detection suitable for practical applications.
在工业应用中，机器人手臂抓取检测任务常常面临精度和成功率不足的问题，这导致操作效率降低。尽管现有方法已取得了一些成功，但在检测精度、实时性能和泛化能力方面仍存在局限性。为应对这些挑战，本文提出了一种基于CenterNet框架的增强型抓取检测模型G-RCenterNet。首先，引入通道和空间注意力机制以提高网络提取目标特征的能力，显著提升复杂背景下的抓取检测性能。其次，提出一种高效的注意力模块搜索策略来取代传统的全连接层结构，这不仅提高了检测精度，还降低了计算开销。此外，在预测解码阶段引入GSConv模块以加快推理速度，同时保持高精度，进一步提高实时性能。最后，选择ResNet50作为骨干网络，并专门为抓取检测任务设计了自定义损失函数，显著增强了模型预测可行抓取框的能力。所提出的G-RCenterNet算法被嵌入到一个机器人抓取系统中，其中结构化光深度相机捕获目标图像，抓取检测网络预测最优抓取框。基于康奈尔抓取数据集和实际场景的实验结果表明，G-RCenterNet模型在抓取检测任务中表现稳健，实现了适用于实际应用的准确高效的目标抓取检测。
