基于深度学习的采摘机器人目标识别定位算法

Target recognition and positioning algorithm of picking robot based on deep learning

为了提升采摘机器人对果实的识别准确率以及定位定精度,提出一种基于深度学习Faster-RCNN框架的采摘机器人目标识别和定位算法。首先采用卷积神经网络VGG16模型提取输入图像的特性信息,并利用区域提议网络RPN生成含有目标的候选框,通过引入自适应候选框数的方法有效提升了算法性能,然后利用多任务损失函数对目标进行分类识别和预测框校正定位,从而得到目标在图像坐标系统的高精准度坐标,最后通过标定求解出采摘机器人手眼两个坐标系之间的映射关系,从而实现了对果实的精确识别和定位。通过对苹果的识别和定位实验结果表明,所提算法具有较高的识别度,平均精度达97.5%,且定位误差更低,最大误差仅为1.33cm,可为智慧农业发展提供有力的技术支持。

In order to improve the recognition accuracy and positioning accuracy of fruit picking robot,a target recognition and positioning algorithm based on deep learning Faster-RCNN framework was proposed.Firstly,the convolutional neural network VGG16model was used to extract the characteristics information of the input image,and the region proposal network RPN was used to generate the candidate box containing the target.The adaptive number of candidate boxes was introduced to improve the performance of the algorithm.Then,the multi task loss function was used to classify the target and correct the prediction box.Finally,the mapping relationship between the two coordinate systems of the hand and eye of the picking robot was solved by calibration,so as to realize the accurate recognition and positioning of the fruit.The experimental results of apple recognition and location show that the proposed algorithm has high recognition accuracy,the average accuracy is 97.5%,and the location error is lower,the maximum error is only 1.33cm,which can provide strong technical support for the development of smart agriculture.