一种基于偏好免疫网络多特征辨识的油茶果分选识别方法

A method of Camellia fruit sorting based on multi-features identification in preference aiNet

针对油茶果采摘、脱壳后机器视觉分选效率不高的问题,提出一种多特征偏好人工免疫网络算法,该算法应用人工免疫网络的多目标优化与偏好数据库特征,提取油茶目标的颜色、形态多特征输入免疫网络进行仿真测试。试验结果表明,本文提出的多特征偏好免疫网络的识别率最高达到90%以上。相比单特征分选方法有了较大的提升,证明本文分选方法的有效性,并为农林业目标智能化分选辨识提供一种可行的方案。

In order to solve the problem of low sorting efficiency after picking and shelling of Camellia fruits in machine-vision,the paper proposes a preference artificial immune network algorithm(aiNet)with multi-features,which applies the multi-objective optimization of artificial immune network and preference database to extract the multi-features in color and shape of Camellia objects to input into the immune network for simulation test.The test results show that the multi-features preference immune network proposed in this paper is feasible.The recognition rate of the network has reached 90%,and the minimum identification time is 60 ms.Compared with the single feature sorting method,this method is more effective,which provides a feasible scheme for the intelligent sorting method of agricultural and forestry targets.