基于机器视觉的牡蛎分级设备设计

Research on oyster grading equipment based on machine vision

目的:提高牡蛎分级的精确性和全面性。方法:提出并设计了牡蛎自动化分级设备,确定了旋转滚筒与挡板传送带结合的牡蛎排队结构、质量检测和机器视觉检测相结合的分级方式,完成了牡蛎分级设备的整体结构设计。通过工业相机采集牡蛎图像,使用大津法二值化、高斯滤波处理、Canny算子边缘提取等方法提取牡蛎图像,通过机器视觉算法以长度和饱满度为标准对牡蛎进行分级,并进行机器视觉分级与人工分级对比试验。结果:该设备分级准确率为95.4%,图像检测速度约为0.647 s/幅。结论:机器视觉对牡蛎分级是有效的,可以较为准确地对牡蛎进行分级。

Objective:To improve the accuracy and comprehensiveness of oyster grading.Methods:The oyster automatic grading equipment was proposed and designed,the oyster queuing structure combining the rotating drum and the baffle conveyor belt,the grading method combining weight detection and machine vision detection were determined,and the overall structure design of the oyster grading equipment was completed.The oyster image was collected by industrial camera,and the oyster image was extracted by Otsu binarization,Gaussian filtering processing,Canny operator edge extraction and other methods.The oyster was graded by machine vision algorithm with length and fullness as the standard,and the comparison test between machine vision grading and manual grading were carried out.Results:The machine vision classification accuracy of oysters was 95.4%,and the image detection speed was about 0.647 s/image.Conclusion:Machine vision is effective for oyster grading and can classify oysters more accurately.