基于机器视觉的百香果品质多指标在线检测与分选

Online detection and sorting of passion fruit quality based on machine vision using multi-indicator

[目的]提高百香果品质分级的精细化与智能化水平。[方法]利用OpenCV与轻量化神经网络(MobileNetV3_large_ssld)对百香果的果径、成熟度与皱缩情况进行了检测。通过最小外接矩形的短边测量果径;在HSV颜色空间中,分析H分量值在特定范围内(H∈[0,10]∪[156,180],[11,25],[26,34],[125,155])的像素占比来判别果实的成熟度;建立MobileNetV3_large_ssld轻量化神经网络模型,用于判别果皮的皱缩情况。基于对果径、成熟度与皱缩情况3个指标的检测结果,进一步采用评级量表法构建了果实品质综合评价模型,并开发了在线检测及分选系统。该系统利用KNN背景减去模型从传送带上运动的百香果视频流中提取目标并去除果柄,采用区间取帧法从视频中捕获单张图像,依据多指标综合评价模型对百香果品质进行分级,并通过分选机构的拨爪将百香果拨至相应的等级通道。[结果]测试结果显示,系统分选与人工分选的整体吻合度为97.02%,特级吻合度为95.51%,一级吻合度为97.84%,二级吻合度为100.00%。[结论]该系统可用于不同等级百香果品质的在线检测与分选。

[Objective]Refining the accuracy and intelligence of passion fruit quality assessment.[Methods]The study used the capabilities of OpenCV along with a compact neural network architecture,MobileNetV3_large_ssld,to accurately determine the fruit s diameter,ripeness,and the degree of its wrinkling.The diameter measurement was achieved by analyzing the short side of the fruit s minimum bounding rectangle.The ripeness assessment was based on the pixel ratio of H component values within specific ranges(H∈[0,10]∪[156,180],[11,25],[26,34],[125,155])in the HSV color space.Furthermore,the study developed a MobileNetV3_large_ssld model to evaluate the wrinkling of the fruit s skin.Leveraging these three key indicators,a comprehensive fruit quality evaluation model was established using a rating scale approach,and an online detection and sorting system was subsequently developed.This system employed KNN background subtraction to extract the fruits target,excludes stems,and used interval frame sampling method to capture single image for each fruit from the video.The comprehensive evaluation model was utilized to assess the quality of passion fruits,which were then sorted into their appropriate grade channels through a sorting mechanism.[Results]The test results indicated a high degree of consistency between the system s sorting and manual sorting,with an overall accuracy of 97.02%.The consistency rates for top-grade fruits,first-grade,and second-grade fruits were 95.51%,97.84%,and 100%,respectively.[Conclusion]This system could be used for online detection and sorting of passion in different quality grades.