Objective: To solve the problems of low detection efficiency and poor accuracy of existing automatic detection methods for egg defect images. Methods: Based on the egg detection system, an improved YOLOv5 automatic detection model was proposed. Added the lightweight network MobileNetv3 to YOLOv5 model to reduce the complexity of the model, and deleted the neck network and small target detection at the output end. Results: Compared with the traditional control method, this method can detect the surface defects of egg targets more accurately and efficiently, with the complexity of more than 35% reducing, the detection time of a single image of 14.25 ms, and the detection accuracy rate over 95%, which meet the needs of food defect detection. Conclusion: The improved YOLOv5 detection model can effectively improve the detection efficiency of egg defects.

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[1] 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 grading of potato external defects based on lightweight convolution network[J].Food Science,2021,42(10):284-289.
[2] 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 convolution neural network to detect peanut kernel quality[J].Journal of Agricultural Engineering,2020,36(4):269-277.
[3] 海潮,赵凤霞,孙烁.基于Blob分析的红枣表面缺陷在线检测技术[J].食品与机械,2018,34(1):126-129.HAI C,ZHAO F X,SUN S.On line detection technology of jujube surface defects based on Blob analysis[J].Food & Machinery,2018,34(1):126-129.
[4] 张涛,刘玉婷,杨亚宁,等.基于机器视觉的表面缺陷检测研究综述[J].科学技术与工程,2020,20(35):14 366-14 376.ZHANG T,LIU Y T,YANG Y N,et al.Review of research on surface defect detection based on machine vision[J].Science Technology and Engineering,2020,20(35):14 366-14 376.
[5] 肖旺,杨煜俊,申启访,等.基于改进的GoogLeNet鸭蛋表面缺陷检测[J].食品与机械,2021,37(6):162-167.XIAO W,YANG Y J,SHEN Q F,et al.Detection of duck egg surface defects based on improved GoogLeNet[J].Food & Machinery,2021,37(6):162-167.
[6] 杨志锐,郑宏,郭中原,等.基于网中网卷积神经网络的红枣缺陷检测[J].食品与机械,2020,36(2):140-145,181.YANG Z R,ZHENG H,GUO Z Y,et al.Chinese date defect detection based on net in net convolution neural network[J].Food & Machinery,2020,36(2):140-145,181.
[7] 王云鹏,司海平,宋佳珍,等.基于红外与可见光图像融合的苹果表面缺陷检测方法[J].食品与机械,2021,37(12):127-131.WANG Y P,SI H P,SONG J Z,et al.Apple surface defect detection method based on infrared and visible image fusion[J].Food & Machinery,2021,37(12):127-131.
[8] 薛勇,王立扬,张瑜,等.基于GoogLeNet深度迁移学习的苹果缺陷检测方法[J].农业机械学报,2020,51(7):30-35.XUE Y,WANG L Y,ZHANG Y,et al.Apple defect detection method based on GoogLeNet deep migration learning[J].Journal of Agricultural Machinery,2020,51(7):30-35.
[9] 周靖宇,孙锐,余多,等.基于近红外技术和偏最小二乘判别法对无花果成熟度的快速判别[J].食品与机械,2020,36(11):107-111.ZHOU J Y,SUN R,YU D,et al.Rapid identification of fig maturity based on near infrared technology and partial least squares method[J].Food & Machinery,2020,36(11):107-111.
[10] 张铮,熊盛辉,王孙强,等.基于机器视觉的香蕉果肉缺陷预测方法[J].食品与机械,2020,36(7):150-154.ZHANG Z,XIONG S H,WANG S Q,et al.Prediction method of banana pulp defects based on machine vision[J].Food & Machinery,2020,36(7):150-154.
[11] 张义志,王瑞,张伟峰,等.高光谱技术检测农产品成熟度研究进展[J].湖北农业科学,2020,59(12):5-8,12.ZHANG Y Z,WANG R,ZHANG W F,et al.Research progress on detecting maturity of agricultural products by hyperspectral technology[J].Hubei Agricultural Science,2020,59(12):5-8,12.
[12] 杨晨昱,袁鸿飞,马惠玲,等.基于傅里叶近红外光谱和电子鼻技术的苹果霉心病无损检测[J].食品与发酵工业,2021,47(7):211-216.YANG C Y,YUAN H F,MA H L,et al.Nondestructive detection of apple mycoheart disease based on Fourier near infraredspectroscopy and electronic nose technology[J].Food and Fermentation Industry,2021,47(7):211-216.
[13] 任二芳,牛德宝,温立香,等.电子鼻和电子舌在水果检测中的应用进展[J].食品工业,2019,40(10):261-264.REN E F,NIU D B,WEN L X,et al.Application progress of electronic nose and electronic tongue in fruit detection[J].Food Industry,2019,40(10):261-264.
[14] 赵小霞,李志强.基于PLC和机器视觉的水果自动分级系统研究[J].农机化研究,2021,12(8):75-79.ZHAO X X,LI Z Q.Research on automatic fruit grading system based on PLC and machine vision[J].Agricultural Mechanization Research,2021,12(8):75-79.
[15] 刘芳,刘玉坤,林森,等.基于改进型YOLO的复杂环境下番茄果实快速识别方法[J].农业机械学报,2020,51(6):229-237.LIU F,LIU Y K,LIN S,et al.Rapid identification method of tomato fruit in complex environment based on improved Yolo[J].Transactions of the Chinese Society for Agricultural Machinery,2020,51(6):229-237.
[16] 赵利平,吴德刚.融合GA的三点定位夜间苹果目标的识别算法研究[J].中国农机化学报,2020,41(5):134-138.ZHAO L P,WU D G.Research on recognition algorithm of three-point positioning night apple target based on GA[J].Journal of Chinese Agricultural Mechanization,2020,41(5):134-138.
[17] 陶浩,李笑,陈敏.基于改进ORB特征的遥操作工程机器人双目视觉定位[J].测控技术,2019,38(7):19-23.TAO H,LI X,CHEN M.Binocular vision of teleoperation engineering robot based on improved ORB feature[J].Measurement & Control Technology,2019,38(7):19-23.
[18] 宋海涛,何文浩,原魁.一种基于SIFT特征的机器人环境感知双目立体视觉系统[J].控制与决策,2019,34(7):1 545-1 552.SONG H T,HE W H,YUAN K.A robot environment perception binocular stereo vision system based on SIFT feature[J].Control and Decision,2019,34(7):1 545-1 552.
[19] 冯喆,李卫豪,崔笛.基于高光谱成像和深度学习的山核桃内源性异物检测[J].农业机械学报,2021,52(S0):466-471.FENG Z,LI Z H,CUI D.Detection of endogenous foreign bodies in pecan based on hyperspectral imaging and deep learning[J].Journal of Agricultural Machinery,2021,52(S0):466-471.
[20] 王红霞,周家奇,辜承昊,等.用于图像分类的卷积神经网络中激活函数的设计[J].浙江大学学报(工学版),2019,53(7):1 363-1 373.WANG H X,ZHOU J Q,GU C H,et al.Design of activation function in convolutional neural network for image classification[J].Journal of Zhejiang University(Engineering Edition),2019,53(7):1 363-1 373.
[21] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1 137-1 149.

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