基于多目标采样和改进Mask R-CNN的木瓜成熟度检测

Papaya maturity detection based on multi-target sampling and improved Mask R-CNN

目的:提高木瓜成熟度检测准确率及鲁棒性。方法:提出一种基于多目标采样和改进Mask R-CNN的木瓜成熟度检测方法。通过均值平均精度、准确率、精确率—召回率曲线和计算时间等指标,验证所提方法的有效性和鲁棒性,并将其检测效果与Faster R-CNN、RetinaNet和CenterMask等方法进行对比。结果:试验方法对木瓜成熟度检测的平均精度均值、50%平均精度均值、75%平均精度均值分别为98.43%,98.67%,98.68%,对未成熟、半成熟和成熟木瓜成熟度的平均检测精度为99.38%,98.81%,99.37%。结论:该方法可用于开发木瓜成熟度检测的电子系统,提升木瓜成熟度检测和木瓜分级的性能。

Objective:Improve the accuracy and robustness of papaya ripeness detection.Methods:A method of papaya ripeness detection based on multi-target sampling and improved Mask R-CNN was proposed.In the process of data expansion,the method introduced multi-object sampling technology to generate enhanced images from small data sets taken under controlled conditions,which was conducive to extending the proposed method to data sets with complex features of actual papaya images.The effectiveness and robustness of the proposed method were verified by means of average accuracy,accuracy,accuracy-recall curve and calculation time,and the results of papaya ripeness detection effect were compared with those of Faster R-CNN,RetinaNet and CenterMask.Results:The values of mean awerage precision,50%mean awerage precision and 75%mean awerage precision for the papaya ripeness detection were 98.43%,98.67%and 98.68%,respectively.The average accuracies for the ripeness detection of immature,semi-mature and mature papayas were 99.38%,98.81%and 99.37%,respectively.Conclusion:This method can be used to develop an electronic system for papaya ripeness detection and improve the performance of papaya ripeness detection and grading.