获取渔业养殖鱼类生长态势的人工测量方法费时费力,且影响鱼的正常生长。为了实现水下鱼体信息动态感知和快速无损检测,该研究提出立体视觉下动态鱼体尺寸测量方法。通过双目立体视觉技术获取三维信息,再通过Mask-RCNN(Mask Region Conv...获取渔业养殖鱼类生长态势的人工测量方法费时费力,且影响鱼的正常生长。为了实现水下鱼体信息动态感知和快速无损检测,该研究提出立体视觉下动态鱼体尺寸测量方法。通过双目立体视觉技术获取三维信息,再通过Mask-RCNN(Mask Region Convolution Neural Network)网络进行鱼体检测与精细分割,最后生成鱼表面的三维点云数据,计算得到自由活动下多条鱼的外形尺寸。试验结果表明,长度和宽度的平均相对误差分别在4.7%和9.2%左右。该研究满足了水产养殖环境下进行可视化管理、无接触测量鱼体尺寸的需要,可以为养殖过程中分级饲养和合理投饵提供参考依据。展开更多
通过图像处理对植物叶片进行分割是研究植物表型性状的基础,但叶片间相互遮挡、叶片边缘特征不明显以及幼叶目标过小会对叶片分割效果造成很大的障碍。针对上述问题,该研究提出了一种基于改进MaskR-CNN模型的植物叶片分割方法,通过引入...通过图像处理对植物叶片进行分割是研究植物表型性状的基础,但叶片间相互遮挡、叶片边缘特征不明显以及幼叶目标过小会对叶片分割效果造成很大的障碍。针对上述问题,该研究提出了一种基于改进MaskR-CNN模型的植物叶片分割方法,通过引入级联检测模块对模型检测分支进行改进,以提高遮挡叶片检测质量;利用注意力机制和一个2层3×3卷积模块对模型分割分支进行优化,以提高边缘特征表达能力;在模型测试过程中采用多尺度叶片分割策略,利用多个尺度上的最优目标以分割幼叶。测试结果表明,在CVPPP叶片分割挑战数据集中的对称最佳Dice得分Symmetric Best Dice(SBD)为90.3%,相比于利用域随机化数据增强策略并采用MaskR-CNN模型进行叶片实例分割的方法提高了2.3个百分点,叶片分割效果有显著提升。该研究提出的方法可以有效解决植物叶片分割效果不佳的问题,为植物表型研究提供技术支撑。展开更多
The accurate identification,detection,and segmentation of tea buds and leaves are important factors for realizing intelligent tea picking.A tea picking point location method based on the region-based convolutional neu...The accurate identification,detection,and segmentation of tea buds and leaves are important factors for realizing intelligent tea picking.A tea picking point location method based on the region-based convolutional neural network(R-CNN)Mask-RCNN is proposed,and a tea bud and leaf and picking point recognition model is established.First,tea buds and leaf pictures are collected in a complex environment,the Resnet50 residual network and a feature pyramid network(FPN)are used to extract bud and leaf features,and preliminary classification and preselection box regression training-performed on the feature maps through a regional proposal network(RPN).Second,the regional feature aggregation method(RoIAlign)is used to eliminate the quantization error,and the feature map of the preselected region of interest(ROI)is converted into a fixed-size feature map.The output module of the model realizes the functions of classification,regression and segmentation.Finally,through the output mask image and the positioning algorithm the positioning of the picking points of tea buds and leaves is determined.One hundred tea tree bud and leaf pictures in a complex environment are selected for testing.The experimental results show that the average detection accuracy rate reaches 93.95%and that the recall rate reaches 92.48%.The tea picking location method presented in this paper is more versatile and robust in complex environments.展开更多
文摘获取渔业养殖鱼类生长态势的人工测量方法费时费力,且影响鱼的正常生长。为了实现水下鱼体信息动态感知和快速无损检测,该研究提出立体视觉下动态鱼体尺寸测量方法。通过双目立体视觉技术获取三维信息,再通过Mask-RCNN(Mask Region Convolution Neural Network)网络进行鱼体检测与精细分割,最后生成鱼表面的三维点云数据,计算得到自由活动下多条鱼的外形尺寸。试验结果表明,长度和宽度的平均相对误差分别在4.7%和9.2%左右。该研究满足了水产养殖环境下进行可视化管理、无接触测量鱼体尺寸的需要,可以为养殖过程中分级饲养和合理投饵提供参考依据。
文摘通过图像处理对植物叶片进行分割是研究植物表型性状的基础,但叶片间相互遮挡、叶片边缘特征不明显以及幼叶目标过小会对叶片分割效果造成很大的障碍。针对上述问题,该研究提出了一种基于改进MaskR-CNN模型的植物叶片分割方法,通过引入级联检测模块对模型检测分支进行改进,以提高遮挡叶片检测质量;利用注意力机制和一个2层3×3卷积模块对模型分割分支进行优化,以提高边缘特征表达能力;在模型测试过程中采用多尺度叶片分割策略,利用多个尺度上的最优目标以分割幼叶。测试结果表明,在CVPPP叶片分割挑战数据集中的对称最佳Dice得分Symmetric Best Dice(SBD)为90.3%,相比于利用域随机化数据增强策略并采用MaskR-CNN模型进行叶片实例分割的方法提高了2.3个百分点,叶片分割效果有显著提升。该研究提出的方法可以有效解决植物叶片分割效果不佳的问题,为植物表型研究提供技术支撑。
基金the Key Research and Development Project of Anhui Province(1804a07020108,201904a06020056,202104a06020012)Independent Project of Anhui Key Laboratory of Smart Agricultural Technology and Equipment(APKLSATE2019X001).
文摘The accurate identification,detection,and segmentation of tea buds and leaves are important factors for realizing intelligent tea picking.A tea picking point location method based on the region-based convolutional neural network(R-CNN)Mask-RCNN is proposed,and a tea bud and leaf and picking point recognition model is established.First,tea buds and leaf pictures are collected in a complex environment,the Resnet50 residual network and a feature pyramid network(FPN)are used to extract bud and leaf features,and preliminary classification and preselection box regression training-performed on the feature maps through a regional proposal network(RPN).Second,the regional feature aggregation method(RoIAlign)is used to eliminate the quantization error,and the feature map of the preselected region of interest(ROI)is converted into a fixed-size feature map.The output module of the model realizes the functions of classification,regression and segmentation.Finally,through the output mask image and the positioning algorithm the positioning of the picking points of tea buds and leaves is determined.One hundred tea tree bud and leaf pictures in a complex environment are selected for testing.The experimental results show that the average detection accuracy rate reaches 93.95%and that the recall rate reaches 92.48%.The tea picking location method presented in this paper is more versatile and robust in complex environments.
