基于机器视觉和YOLOv4的破损鸡蛋在线检测研究

破损鸡蛋导致的漏液会污染自动化生产线和完好鸡蛋,不仅影响生产效率,还会干扰裂纹鸡蛋的检测.为实现破损鸡蛋快速、准确、低成本的识别,本文利用机器视觉技术,并结合深度学习网络深层次特征提取、高精度检测分类的特性,提出一种基于YOLOv4网络的破损鸡蛋检测方法.构建破损鸡蛋图像数据集,搭建YOLOv4深度学习网络,训练含有破...

Research on On-line Detection of Damaged Eggs based on Machine Vision and YOLOv4

The leakage caused by damaged eggs will pollute the automatic production line and intact eggs,which will not only affect the production efficiency,but also interfere with the detection of cracked eggs.In order to realize the fast,accurate and low-cost recognition of damaged eggs,this paper proposes a damaged egg detection method based on YOLOv4 network by using machine vision technology,combined with the characteristics of deep-seated feature extraction and high-precision detection and classification of deep learning network.Build the damaged egg image data set,build the YOLOv4 deep learning network,and train the classification model containing damaged egg and intact egg images;The recognition accuracy of YOLOv4,YOLOv3 and faster RCNN network models for damaged eggs was compared;At the same time,in order to verify the online detection ability of YOLOv4,simulate and build the actual egg production environment,and compare the detection accuracy under different broken egg proportion and different moving speed.The results are as follows:under the same data set,the recognition accuracy of YOLOv4 is 4.62%higher than the average value of YOLOv3 and faster RCNN network model;In online detection,the average recognition accuracy of YOLOv4 model for damaged eggs with different proportions is 86.22%;When the moving speed of egg production line is 5~6 m/min,the average recognition accuracy is 84.91%.The results show that the damaged egg detection method based on YOLOv4 proposed in this paper has good detection effect and high detection rate for eggs moving on the convective waterline.It provides a new method for intelligent production and quality detection of eggs,and has certain practical value.