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基于深度学习目标测定的大蒜收获切根装置设计与试验

Design and Experiment of Garlic Harvesting and Root Cutting Device Based on Deep Learning Target Determination

  • 摘要: 为研究适用于大蒜联合收获的智能化切根装置,提出了基于机器视觉的非接触式定位切根方法,设计了一种基于深度卷积神经网络的大蒜切根试验台。试验台采用深度学习的方法,对采集到的图像进行目标检测,利用APP完成人机交互和结果显示,由深度卷积神经网络给定切根的切入位置,电机控制系统自动调整定位双圆盘切根刀完成切根处理。目标比较试验表明:鳞茎、根盘和蒜根3种目标中,鳞茎可用率为94.79%、置信度得分为0.976 97,适合作为检测目标;检测模型比较试验表明:对比基于Faster R-CNN、SSD、YOLO v2、YOLO v3和YOLO v4算法的10种模型,选择ResNet50作为特征提取网络改进的YOLO v2模型,兼顾检测速度与精度(测试程序中的检测时间为0.052 3 s、置信度得分为0.968 49);切根试验表明:以鳞茎作为目标,采用改进的YOLO v2模型,置信度得分为0.970 99,可用率为96.67%,切根合格率为95.33%,APP中的检测时间为0.088 7 s,满足大蒜联合收获切根要求。

     

    Abstract: In order to study a suitable intelligent root cutting device for garlic combined harvesting, a non-contact bulb root cutting method with machine vision was proposed, and a garlic root cutting test bench based on a deep convolutional neural network was designed afterwards. Specially, the test bench adopted a deep learning theory to perform target detection on the collected images, through using the APP software in Matlab to complete the human-computer interaction. Then, the results presented that the deep convolutional neural network could determine the cutting position of the garlic root, and the motor control system could adjust the position of the double disc cutting automatically, ensuring the root cutting process completed by the root knife. Target comparison tests showed that bulb(availability rate was 94.79%, confidence score was 0.976 97) was suitable for detecting, among the three kinds of bulb, root plate and garlic root. Comparison tests of detection models performed with ten models based on Faster R-CNN, SSD, YOLO v2, YOLO v3 and YOLO v4. The improved YOLO v2 model combined the detection speed and accuracy(the detection time in the test program was 0.052 3 s, and the confidence score was 0.968 49), where ResNet50 was selected as the feature extraction network; by using the improved YOLO v2 model, the root cutting test took bulbs as the targets(the confidence score was 0.970 99, the availability rate was 96.67%, the qualified rate of cutting roots was 95.33%, and the detection time in the APP was 0.088 7 s), can meet the requirements of garlic combined harvesting and cutting roots.

     

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