基于深度学习的钢筋绑扎机器人目标识别定位

为了解决钢筋绑扎机器人对绑扎点识别准确率低，定位精度差的问题，提出一种基于深度学习的钢筋绑扎机器人目标识别与定位方法。首先采用YOLOv4算法对绑扎点目标框识别和裁剪，完成绑扎点初始定位；其次设计轮廓角点选取方法，利用角点计算绑扎点的图像坐标；之后通过融入CBAM注意力机制改进Monodepth算法的特征提取部分，解码部分引入路径增强PAN结构，以提高模型的特征提取能力，进一步提高立体匹配精度；最后通过双目立体视觉定位技术获得绑扎点深度信息，并由坐标变换求解钢筋绑扎机器人手眼坐标系映射关系，从而实现对绑扎点的精确识别和定位。实验结果表明：该方法针对绑扎点目标框的识别准确率达到了99.75%，每秒传输帧数达到54.65；在空间中的定位精度最大误差为11.6mm。可较好地识别定位绑扎点位置，为自动绑扎工作提供有力支持。

Target recognition and location of steel bar binding robot based on deep learning

In order to solve the problem of low recognition accuracy and poor positioning accuracy of steel bar binding robot, a target recognition and positioning method of steel bar binding robot based on deep learning is proposed. Firstly, YOLOv4 algorithm is used to identify and cut the target frame of binding point, and the initial positioning of binding point is completed. Secondly, the contour corner selection method is designed to calculate the image coordinates of the binding points by corner points. Then the feature extraction part of Monodepth algorithm is improved by integrating CBAM attention mechanism, and the decoding part introduces path aggregation network structure to improve the feature extraction ability of the model and further improve the stereo matching accuracy. Finally, the depth information of binding points is obtained by binocular stereo vision positioning technology, and the coordinate transformation is used to solve the mapping relationship between the hand-eye coordinate system of the steel bar binding robot, so as to realize the accurate identification and positioning of binding points. The experimental results show that the recognition accuracy of this method for binding point target frame reaches 99.75%, and the number of frames per second reaches 54.65. The Maximum error of positioning accuracy in space is 11.6mm. It can better identify and locate the binding point position, and provide strong support for automatic binding work.