基于3D结构光的芒果位姿判别与体积质量预测

Mango position detection volume and quality prediction based on 3D structural light

目前大部分芒果需通过人工识别体积质量来实现分拣和分级,造成效率低下、缺乏数据管理。机器视觉是提高芒果分级效率的有效手段,然而传统的工业相机仅能获取二维投影。针对这一情况,利用3D结构光系统,获取芒果的形状描述子结合三维深度信息。然后以80个矫正集为样本,利用Fisher判定方法进行位姿检测,并由非线性支持向量机建立“平躺”、“直立”两种位姿下的体积和质量预测模型,并对20个预测集进行误差分析。结果表明,加入深度信息后,位姿检测的准确率可提高到100%,体积质量的平均误差降低到5%以内。

At present,most mangoes still need to be sorted and graded through manual identification of volume and quality,resulting in low efficiency and lack of data management.Machine vision is an effective means to improve the efficiency of mango grading,but traditional industrial cameras can only obtain two-dimensional projections.In response to this situation,this paper uses a 3 D structured light system to obtain mango shape descriptors combined with three-dimensional depth information.Then 80 correction sets are used as samples,and the fisher judgment method is used for pose detection,and the non-linear support vector machine establishes the volume and mass prediction models in the“flat”and“upright”poses.Error analysis is performed on the prediction set.The results show that after adding depth information,the accuracy of pose detection can be increased to 100%,and the average error of volume quality can be reduced to less than 5%.