基于改进PSO-SVM的生产线分拣机器人罐装食品识别方法

目的:解决现有食品生产线分拣机器人目标识别方法存在的准确率差和效率低等问题。方法:在对基于双目视觉食品分拣系统进行分析的基础上，提出了一种将改进的粒子群算法和支持向量机相结合用于食品分拣机器人的目标识别。通过改进粒子群算法寻优支持向量机参数，获得优化的支持向量机分类模型，对全局特征和局部特征分别进行分类器训练，动态分配特征权重系数，得到最佳识别率。通过试验分析所提方法的性能，验证其可行性。结果:与常规方法相比，所提方法在食品分拣机器人的目标识别中具有较高的识别精度和效率，准确率为99.50%,平均识别时间为0.048 s,满足机器人的分拣需要。结论:所提方法能有效识别罐装食品，提高了分拣机器人分拣准确率和效率。

Identification method of canned food for production line sorting robot based on improved PSO-SVM

Objective: To solve the problems of poor accuracy and low efficiency in target recognition methods for existing sorting robots in food production lines. Methods: On the basis of the analysis of the binocular vision food sorting system, a combination of improved particle swarm optimization algorithm and support vector machine was proposed for target recognition of food sorting robots. By improving the particle swarm optimization algorithm to optimize support vector machine parameters, an optimized support vector machine classification model was obtained. The classifier was trained for both global and local features, and feature weight coefficients were dynamically assigned to obtain the best recognition rate. Analyzed the performance of the proposed method through experiments, and verified its feasibility. Results: Compared with conventional methods, the proposed method had high recognition accuracy and efficiency in target recognition of food sorting robots, with an accuracy rate of 99.50% and an average recognition time of 0.048 s, which meet the needs of robot sorting. Conclusion: The proposed method can effectively identify canning, improved sorting accuracy and efficiency of sorting robots.