To balance the inference speed and detection accuracy of a grasp detection algorithm, which are both important for robot grasping tasks, we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network (AE-GDN). Three spatial attention modules are introduced in the encoder stages to enhance the detailed information, and three channel attention modules are introduced in the decoder stages to extract more semantic information. Several lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN. A high intersection over union (IoU) value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration, but might cause a collision. This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers. We design a new IoU loss calculation method based on an hourglass box matching mechanism, which will create good correspondence between high IoUs and high-quality grasp configurations. AEGDN achieves the accuracy of 98.9% and 96.6% on the Cornell and Jacquard datasets, respectively. The inference speed reaches 43.5 frames per second with only about 1.2 × 10⁶ parameters. The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well. Codes are available at https://github.com/robvincen/robot_gradet.

为了平衡抓取检测算法的推理速度和检测精度（这对于机器人抓取任务都很重要），我们提出了一种编码器-解码器结构的像素级抓取检测神经网络，称为基于注意力的高效机器人抓取检测网络（AE- GDN）。在编码器阶段引入三个空间注意模块以增强详细信息，在解码器阶段引入三个通道注意模块以提取更多语义信息。使用多个轻量级且高效的DenseBlock来连接编码器和解码器路径，以提高AE-GDN的特征建模能力。预测抓取矩形与地面实况之间的高交并集 (IoU) 值并不一定意味着高质量的抓取配置，但可能会导致碰撞。这是因为传统的 IoU 损失计算方法将预测矩形的中心部分视为与夹具周围区域具有相同的重要性。我们设计了一种基于沙漏盒匹配机制的新 IoU 损失计算方法，这将在高 IoU 和高质量抓取配置之间建立良好的对应关系。AEGDN 在 Cornell 和 Jacquard 数据集上的准确率分别达到 98.9% 和 96.6%。推理速度达到43.5帧/秒，参数仅约1.2×10 ⁶。所提出的 AE-GDN 也已部署在实用的机械臂抓取系统上，并表现良好。代码可在 https://github.com/robvincen/robot_gradet 获取。