| 周 朔,宋飞虎,李臻峰,李 静.基于 3D 结构光的芒果位姿判别与体积质量预测[J].电子测量与仪器学报,2022,36(2):49-56 |
| 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 predictionbased on 3D structural light |
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| DOI： |
| 中文关键词: 结构光相机 形状描述子 图像处理 回归模型 Fisher 判定 |
| 英文关键词:structured light camera shape descriptor image processing regression model Fisher judgment |
| 基金项目:国家自然科学基金（21606109）项目资助 |
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| 摘要点击次数: 932 |
| 全文下载次数: 753 |
| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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 3D 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%. |
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