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.

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[1] 姜洪权, 贺帅, 高建民, 等. 一种改进卷积神经网络模型的焊缝缺陷识别方法[J]. 机械工程学报, 2020, 56(8): 235-242. JIANG H Q, HE S, GAO J M, et al. An improved method of welding seam defect recognition based on convolutional neural network model[J]. Chinese Journal of Mechanical Engineering, 2020, 56(8): 235-242.
[2] 项辉宇, 薛真, 冷崇杰, 等. 基于Halcon的苹果品质视觉检测试验研究[J]. 食品与机械, 2016, 32(10): 123-126. XIANG H Y, XUE Z, LENG C J, et al. Experimental study on visual inspection of apple quality based on Halcon[J]. Food & Machinery, 2016, 32(10): 123-126.
[3] 杨森, 冯全, 张建华, 等. 基于轻量卷积网络的马铃薯外部缺陷无损分级[J]. 食品科学, 2021, 42(10): 284-289. YANG S, FENG Q, ZHANG J H, et al. Non-destructive classification of potato external defects based on lightweight convolutional network[J]. Food Science, 2021, 42(10): 284-289.
[4] 张思雨, 张秋菊, 李可. 采用机器视觉与自适应卷积神经网络检测花生仁品质[J]. 农业工程学报, 2020, 36(4): 269-277. ZHANG S Y, ZHANG Q J, LI K. Using machine vision and adaptive convolutional neural network to detect the quality of peanut kernels[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(4): 269-277.
[5] 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. CHENG L. Apple surface defect detection based on improved particle swarm optimization algorithm[J]. Food & Machinery, 2018, 34(3): 141-145.
[6] 王丽荣. 基于视觉技术的机器人抓取目标识别与定位[J]. 机械设计与制造工程, 2021, 50(10): 33-36. WANG L R. Robot grasping target recognition and localization based on visual technology[J]. Mechanical Design and Manufacturing Engineering, 2021, 50(10): 33-36.
[7] 王成军, 韦志文, 严晨. 基于机器视觉技术的分拣机器人研究综述[J]. 科学技术与工程, 2022, 22(3): 893-902. WANG C J, WEI Z W, YAN C. Summary of research on sorting robots based on machine vision technology[J]. Science Technology Engineering, 2022, 22(3): 893-902.
[8] 伍锡如, 黄国明, 孙立宁. 基于深度学习的工业分拣机器人快速视觉识别与定位算法[J]. 机器人, 2016, 38(6): 711-719. WU X R, HUANG G M, SUN L N. A fast visual recognition and location algorithm for industrial sorting robots based on deep learning[J]. Robotics, 2016, 38(6): 711-719.
[9] 王银明, 张丹. 基于并联机器人的单片装火腿缺陷识别与分拣系统设计[J]. 食品与机械, 2022, 38(10): 104-109. WANG Y M, ZHANG D. Design of a single piece ham defect recognition and sorting system based on parallel robots[J]. Food & Machinery, 2022, 38(10): 104-109.
[10] 王新龙, 李翔. 基于分类特征提取和深度学习的牛肉品质识别[J]. 食品与机械, 2022, 38(7): 91-98. WANG X L, LI X. Beef quality recognition based on classification feature extraction and deep learning[J]. Food & Machinery, 2022, 38(7): 91-98.
[11] 刘云, 杨建滨, 王传旭. 基于卷积神经网络的苹果缺陷检测算法[J]. 电子测量技术, 2017, 40(3): 108-112. LIU Y, YANG J B, WANG C X. Apple defect detection algorithm based on convolutional neural network[J]. Electronic Measurement Technology, 2017, 40(3): 108-112.
[12] 周雨帆, 李胜旺, 杨奎河, 等. 基于轻量级卷积神经网络的苹果表面缺陷检测方法[J]. 河北工业科技, 2021, 38(5): 388-394. ZHOU Y F, LI S W, YANG K H, et al. Apple surface defect detection method based on lightweight convolutional neural network[J]. Hebei Industrial Technology, 2021, 38(5): 388-394.
[13] 梅金波, 李涛, 秦寅初. 苹果采摘机器人监测系统和表面缺陷检测方法研究[J]. 计算机测量与控制, 2023, 31(6): 19-26. MEI J B, LI T, QIN Y C. Research on apple picking robot monitoring system and surface defect detection methods[J]. Computer Measurement and Control, 2023, 31(6): 19-26.
[14] 杨双艳, 杨紫刚, 张四伟, 等. 基于近红外光谱和PSO-SVM算法的烟叶自动分级方法[J]. 贵州农业科学, 2018, 46(12): 141-144. YANG S Y, YANG Z G, ZHANG S W, et al. Automatic tobacco grading method based on near infrared spectroscopy and PSO-SVM algorithm[J]. Guizhou Agricultural Sciences, 2018, 46 (12): 141-144.
[15] 王阳阳, 黄勋, 陈浩, 等. 基于同态滤波和改进K-means的苹果分级算法研究[J]. 食品与机械, 2019, 35(12): 47-51, 112. WANG Y Y, HUANG X, CHEN H, et al. Apple grading algorithm based on homomorphic filtering and improved k-means[J]. Food & Machinery, 2019, 35(12): 47-51, 112.
[16] 王立扬, 张瑜, 沈群, 等. 基于改进型LeNet-5的苹果自动分级方法[J]. 中国农机化学报, 2020, 41(7): 105-110. WANG L Y, ZHAN G Y, SHEN Q, et al. Automatic Apple classification method based on improvedlenet-5[J]. Chinese Journal of Agricultural Mechanochemistry, 2020, 41(7): 105-110.
[17] 于蒙, 李雄, 杨海潮, 等. 基于图像识别的苹果的等级分级研究[J]. 自动化与仪表, 2019, 34(7): 39-43. YU M, LI X, YANG H C, et al. Apple grading based on image recognition[J]. Automation and Instrumentation, 2019, 34(7): 39-43.
[18] 樊泽泽, 柳倩, 柴洁玮, 等. 基于颜色与果径特征的苹果树果实检测与分级[J]. 计算机工程与科学, 2020, 42(9): 1 599-1 607. FAN Z Z, LIU Q, CHAI J W, et al. Apple fruit detection and grading based on color and fruit diameter characteristics[J]. Computer Engineering and Science, 2020, 42(9): 1 599-1 607.
[19] 王冉冉, 刘鑫, 尹孟, 等. 面向苹果硬度检测仪的声振信号激励与采集系统设计[J]. 浙江大学学报(农业与生命科学版), 2020, 46(1): 111-118. WANG R R, LIU X, YIN M, et al. Design of acoustic vibration signal excitation and acquisition system for apple hardness tester[J]. Journal of Zhejiang University (Agriculture and Life Sciences Edition), 2020, 46(1): 111-118.
[20] 王泽霞, 陈革, 陈振中. 基于改进卷积神经网络的化纤丝饼表面缺陷识别[J]. 纺织学报, 2020, 41(4): 115-120. WANG Z X, CHEN G, CHEN Z Z. Surface defect recognition of chemical fiber cake based on improved convolutional neural network[J]. Journal of Textile Research, 2020, 41(4): 115-120.
[21] 王博, 刘俊康, 陆逢贵, 等. 基于卷积神经网络的食品图像识别[J]. 食品安全质量检测学报, 2019, 10(18): 6 241-6 247. WANG B, LIU J K, LU F G, et al. Food image recognition based on convolutional neural network[J]. Journal of Food Safety and Quality Testing, 2019, 10(18): 6 241-6 247.



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