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A distributed multiprocessor imaging system for prune defect sorting.

机译:用于修剪缺陷分类的分布式多处理器成像系统。

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摘要

Line-scan imaging was investigated as a means for detection of surface defects on dried prunes. Spectral characteristics of the defects were measured. The degree of discontinuity in gray level was used as the sorting criterion.; A laboratory line-scan imaging system was developed to acquire and process prune images. An algorithm for prune defect detection was developed based on row gradients, local thresholds, and a nonparametric classifier. Emphasis in the algorithm development was on simplicity, to allow for an eventual sorting rate of 20 prunes/s. Classification errors were 8% for scab, 0% for exposed pit, 0% for mold, 1% for crack, 3% for insect injury, and 0% for good prunes with the defective pixel threshold value of TDP = 1.25%. Specular reflectance did not significantly compromise sorting accuracy.; A prototype automatic prune sorter was developed, including a feeder, singulator, illumination chamber, three line-scan cameras, three multiprocessor camera subsystems, master computer, and pneumatic rejector. The defect detection algorithm was simplified to only one feature of defective rows, DR, used for the classification. A typical prune image could be processed in less than 15 ms. When a prune was moved in air at a speed of 2 m/s through the illumination chamber, its entire surface was scanned by three line-scan cameras. High speed image frame grabber circuits with parallel structure were developed to allow the subsystem computers to process the current image while the next image was being digitized. An adaptive image sizing scheme developed was found important for high speed image acquisition, processing, and pneumatic rejection.; Real-time sorting errors were 5.2% for mold, 9.1% for crack, 16.5% for scab, and 3.7% for good prunes with the gradient threshold value of TGRAD = 64 and the defective row threshold value of TDR = 16 at a sorting rate of 10 fruit per second. Greater accuracy could be achieved with the original algorithm and faster computer hardware.
机译:研究了线扫描成像,作为检测干梅表面缺陷的一种手段。测量了缺陷的光谱特性。灰度的不连续度被用作分类标准。开发了实验室线扫描成像系统以获取和处理修剪图像。基于行梯度,局部阈值和非参数分类器,开发了一种用于修剪梅毒的算法。算法开发的重点是简单性,以使最终的分类速率为20修剪/秒。 errors痕的分类错误为8%,凹坑的分类错误为0%,霉菌的分类错误为0%,裂纹的分类为1%,虫害的分类为3%,优质李子的分类错误为0%,缺陷像素阈值TDP = 1.25%。镜面反射率并未显着影响分选精度。开发了原型自动修剪分拣机,包括进料器,分离器,照明室,三个线扫描相机,三个多处理器相机子系统,主计算机和气动剔除器。缺陷检测算法被简化为仅缺陷行的一种特征DR用于分类。典型的修剪图像可以在不到15毫秒的时间内处理。当西梅在空气中以2 m / s的速度通过照明室移动时,其整个表面都由三台线扫描相机扫描。开发了具有并行结构的高速图像采集卡电路,以允许子系统计算机在下一幅图像被数字化时处理当前图像。发现开发的自适应图像大小调整方案对于高速图像获取,处理和气动剔除非常重要。实时分选率对于模具来说是5.2%,对于裂纹来说是9.1%,对于结ab来说是16.5%,对于良好的李子来说,实时分选误差为TGRAD = 64且梯度分选阈值为TDR = 16的不良行阈值每秒10个水果。使用原始算法和更快的计算机硬件可以实现更高的准确性。

著录项

  • 作者

    Tang, Shaoqi.;

  • 作者单位

    University of California, Davis.;

  • 授予单位 University of California, Davis.;
  • 学科 Engineering Agricultural.; Engineering Electronics and Electrical.; Engineering Industrial.
  • 学位 D.Eng.
  • 年度 1990
  • 页码 190 p.
  • 总页数 190
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农业工程;无线电电子学、电信技术;一般工业技术;
  • 关键词

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