| 基于改进粒子群算法的苹果表面缺陷检测 |
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| 引用本文： | 程磊.基于改进粒子群算法的苹果表面缺陷检测[J].食品与机械,2018,34(3):141-145. |
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| 作者姓名： | 程磊 |
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| 作者单位： | 黄淮学院机械与能源工程学院 |
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| 基金项目： | 河南省科技技术厅项目（编号：182102310045）；河南省基础与前沿技术研究项目（编号：1323004130343）；驻马店市2017年工业科技公关项目（编号：17204） |
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| 摘 要： | 通过适应值函数建立了粒子早熟判断机制,自适应调节粒子权重和反余弦策略调整粒子加速因子优化粒子寻优,给出苹果表面缺陷检测流程。试验仿真显示该算法检测苹果表面缺陷的轮廓较为清晰,漏检率测试指标最大为4.5%小于其他算法的,完成漏检率所消耗的时间最少,为苹果质量检测提供了一种新的思路。
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| 关 键 词： | 苹果表面缺陷 粒子群算法 自适应 反余弦 检测研究 |
| 收稿时间： | 2017/7/8 0:00:00 |
Apple surface defect detection research based on improved particle swarm optimization algorithm |
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| CHENGLei.Apple surface defect detection research based on improved particle swarm optimization algorithm[J].Food and Machinery,2018,34(3):141-145. |
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| Authors: | CHENGLei |
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| Affiliation: | Mechanical and Energy Engineering, Huang Huai University, Zhumadian, Henan 463000, China |
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| Abstract: | Premature judgment mechanism of particle swarm is established with fitness function. Adaptive adjustment of particle inertia weigh and acceleration factor of anti cosine strategy are optimized particle swarm to give the apple surface defect process. Simulation results show that improved particle swarm algorithm is more clearer detecting the defect of apple surface, the maximum missing rate test index is 4.5%, and less than other algorithms, and the least time comsuming in complete the missing rate, so that it is the new method for quality detection of apple. |
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| Keywords: | apple surface defect particle swarm optimization adaptive anti cosine detection research |
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