| 摘要: |
| 目的 针对工业场景中传送带上运动物料袋的运动特点， 提出一种在线运动物料袋智能图像计数方法。方法 首先对工业场景中前景图像 “1” 的数目进行统计分析， 并计算其实时概率分布； 然后将该概率分布近似为连续型概率分布， 通过应用线性差值的方法， 计算某时刻前景图像的概率分布， 从而得到该概率分布的密度 S （t） ； 最后比较 S （t） 的大小， 确定通过传送带的在线运动物料袋数目。结果连包计数精度明显提高， 误检测率下降为1%。结论 该方法具有计数精度高的优点， 克服了传统算法的不足， 计数准确率得到了有效提高。 |
| 关键词: 计算机视觉 传送带 运动物料袋 智能图像 |
| DOI： |
| 分类号:TP391 |
| 基金项目:贵州省科技厅、 遵义市科技局、 遵义师范学院联合基金项目 （黔科合 J 字 LKZS[2014]23 号和 08 号）；遵义师范学院教学科研项目 （NO： 13-08 和 13-06） |
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| An Intelligent Image Counting Method of Online Moving Bags |
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JIA Wei1, LIU Wei2,3, LIU Yan-bin1, WANG Ya1, FAN Li-lue1
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1.Zunyi Normal College， Zunyi 563002， China;2.1. Zunyi Normal College， Zunyi 563002， China;3.2. Guizhou University， Guiyang 550025， China
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| Abstract: |
| Objective Targeting at the movement features of bags on conveyer in industrial scene, we proposed an intelligent image counting method for the online moving bags. Methods Firstly, the probability distributions of the number of '1'of the foreground image were computed, and then the probability distribution was approximated to the continuous probability distributions. In order to calculate the probability distribution density S（t） , the linear difference was used to calculate a time probability distribution of the foreground image. Finally, the S（t）values were compared and the moving bag counting was achieved. Results The packet loss problem during bag statistics was solved, and the accuracy of bag counting was significantly improved. The error detection rate dropped to 1%. Conclusion The method has the advantage of high counting accuracy, and it overcomes the shortcomings of traditional algorithms. The accuracy of the counting has been effectively improved. |
| Key words: computer vision conveyor moving bags intelligent image |
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