为减少采摘点定位不当导致末端碰撞损伤结果枝与果串,致使采摘失败及损伤率提高等问题,该研究提出了基于深度学习与葡萄关键结构多目标识别的采摘点定位方法。首先,通过改进YOLACT++模型对结果枝、果梗、果串等葡萄关键结构进行识别与分...为减少采摘点定位不当导致末端碰撞损伤结果枝与果串,致使采摘失败及损伤率提高等问题,该研究提出了基于深度学习与葡萄关键结构多目标识别的采摘点定位方法。首先,通过改进YOLACT++模型对结果枝、果梗、果串等葡萄关键结构进行识别与分割;结合关键区域间的相交情况、相对位置,构建同串葡萄关键结构从属判断与合并方法。最后设计了基于结构约束与范围再选的果梗低碰撞感兴趣区域(region of interest,ROI)选择方法,并以该区域果梗质心为采摘点。试验结果表明,相比于原始的YOLACT++,G-YOLACT++边界框和掩膜平均精度均值分别提升了0.83与0.88个百分点;对单串果实、多串果实样本关键结构从属判断与合并的正确率分别为88%、90%,对关键结构不完整的果串剔除正确率为92.3%;相较于以ROI中果梗外接矩形的中心、以模型识别果梗的质心作为采摘点的定位方法,该研究采摘点定位方法的成功率分别提升了10.95、81.75个百分点。该研究为葡萄采摘机器人的优化提供了技术支持,为非结构化环境中的串类果实采摘机器人的低损收获奠定基础。展开更多
[目的/意义]炭疽病(anthracnose)作为油茶生长过程中重要的病害,其严重程度的精准判定对于精准施药和科学管理具有重大意义。本研究提出了一种改进YOLACT(You Only Look At CoefficienTs)分级模型Camellia-YOLACT,旨在实现对油茶叶片炭...[目的/意义]炭疽病(anthracnose)作为油茶生长过程中重要的病害,其严重程度的精准判定对于精准施药和科学管理具有重大意义。本研究提出了一种改进YOLACT(You Only Look At CoefficienTs)分级模型Camellia-YOLACT,旨在实现对油茶叶片炭疽病感染严重程度的自动、高效判定。[方法]首先在YOLACT主干网络部分使用Swin-Transformer来进行特征提取。Transformer架构的自注意力机制拥有全局感受野及移位窗口等特性,有效地增强了模型的特征提取能力;引入加权双向特征金字塔网络,融合不同尺度的特征信息,加强模型对不同尺度目标的检测能力,提高模型的检测精度;在激活函数的选择上,采用非线性能力更强的HardSwish激活函数替换原模型的ReLu激活函数。由于HardSwish在负值区域不是完全截断,对于输入数据中的噪声具有更高的鲁棒性,自然环境下的图像有着复杂的背景和前景信息,HardSwish的鲁棒性有助于模型更好地处理这些情况,进一步提升精度。[结果和讨论]采用迁移学习方式在油茶炭疽病感染严重程度分级数据集上进行实验验证。消融实验结果表明,本研究提出的Camellia-YOLACT模型的mAP75为86.8%,较改进前提升5.7%;mAPall为78.3%,较改进前提升2.5%;mAR为91.6%,较改进前提升7.9%。对比实验结果表明,Camellia-YOLACT在精度和速度方面表现均好于SOLO(Segmenting Objects by Locations),与Mask R-CNN算法相比,其检测速度提升了2倍。在室外的36组分级实验中进一步验证了Camellia-YOLACT模型的性能,其对油茶炭疽病严重程度的分级正确率达到了94.4%,K值平均绝对误差为1.09%。[结论]本研究提出的Camellia-YOLACT模型在油茶叶片和炭疽病病斑分割上具有较高的精度,能够实现对油茶炭疽病严重程度的自动分级,为油茶病害的精准防治提供技术支持,进一步推动油茶炭疽病诊断的自动化和智能化。展开更多
玉米果穗的表型参数是玉米生长状态的重要表征,生长状况的好坏直接影响玉米产量和质量。为方便无人巡检机器人视觉系统高通量、自动化获取玉米表型参数,该研究基于YOLACT(you only look at coefficients)提出一种高精度-速度平衡的玉米...玉米果穗的表型参数是玉米生长状态的重要表征,生长状况的好坏直接影响玉米产量和质量。为方便无人巡检机器人视觉系统高通量、自动化获取玉米表型参数,该研究基于YOLACT(you only look at coefficients)提出一种高精度-速度平衡的玉米果穗分割模型SwinT-YOLACT。首先使用Swin-Transformer作为模型主干特征提取网络,以提高模型的特征提取能力;然后在特征金字塔网络之前引入有效通道注意力机制,剔除冗余特征信息,以加强对关键特征的融合;最后使用平滑性更好的Mish激活函数替换模型原始激活函数Relu,使模型在保持原有速度的同时进一步提升精度。基于自建玉米果穗数据集训练和测试该模型,试验结果表明,SwinT-YOLACT的掩膜均值平均精度为79.43%,推理速度为35.44帧/s,相较于原始YOLACT和其改进算法YOLACT++,掩膜均值平均精度分别提升了3.51和3.38个百分点;相较于YOLACT、YOLACT++和Mask R-CNN模型,推理速度分别提升了3.39、2.58和28.64帧/s。该模型对玉米果穗有较为优秀的分割效果,适于部署在无人巡检机器人视觉系统上,为玉米生长状态监测提供技术支撑。展开更多
基于深度学习的遥感图像检测在农业生产、军事打击等领域都有所应用。但深度学习模型有计算复杂度高和参数量大的问题,而实际部署深度模型的边缘设备计算性能有限。文章以高分辨率遥感图像作为研究对象,对单阶段的实例分割网络算法进行...基于深度学习的遥感图像检测在农业生产、军事打击等领域都有所应用。但深度学习模型有计算复杂度高和参数量大的问题,而实际部署深度模型的边缘设备计算性能有限。文章以高分辨率遥感图像作为研究对象,对单阶段的实例分割网络算法进行改进,在Yolact(You Only Look At CoefficienTs)网络的基础上提出一种融入注意力机制和可变形卷积的轻量级实例分割算法。使用NWPU VHR-10遥感图像数据集对所提算法进行性能评估,实验结果表明,该算法能在保持性能的情况下减少计算复杂度和参数量。展开更多
The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-r...The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.展开更多
Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving syst...Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems.The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information,which is more accurate and reliable than object detection.However,the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed.Therefore,this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT,which fuses the ICIoU(Improved Complete Intersection over Union)and Res2Net for the YOLACT algorithm.Specifically,the ICIoU function can effectively solve the degradation problem of the original CIoU loss function,and improve the training convergence speed and detection accuracy.The Res2Net module fused with the ECA(Efficient Channel Attention)Net is added to the model’s backbone network,which improves the multi-scale detection capability and mask prediction accuracy.Furthermore,the Cluster NMS(Non-Maximum Suppression)algorithm is introduced in the model’s bounding box regression to enhance the performance of detecting similarly occluded objects.The experimental results demonstrate the superiority of FIR-YOLACT to the based methods and the effectiveness of all components.The processing speed reaches 28 FPS,which meets the demands of real-time vehicle instance segmentation.展开更多
文摘为减少采摘点定位不当导致末端碰撞损伤结果枝与果串,致使采摘失败及损伤率提高等问题,该研究提出了基于深度学习与葡萄关键结构多目标识别的采摘点定位方法。首先,通过改进YOLACT++模型对结果枝、果梗、果串等葡萄关键结构进行识别与分割;结合关键区域间的相交情况、相对位置,构建同串葡萄关键结构从属判断与合并方法。最后设计了基于结构约束与范围再选的果梗低碰撞感兴趣区域(region of interest,ROI)选择方法,并以该区域果梗质心为采摘点。试验结果表明,相比于原始的YOLACT++,G-YOLACT++边界框和掩膜平均精度均值分别提升了0.83与0.88个百分点;对单串果实、多串果实样本关键结构从属判断与合并的正确率分别为88%、90%,对关键结构不完整的果串剔除正确率为92.3%;相较于以ROI中果梗外接矩形的中心、以模型识别果梗的质心作为采摘点的定位方法,该研究采摘点定位方法的成功率分别提升了10.95、81.75个百分点。该研究为葡萄采摘机器人的优化提供了技术支持,为非结构化环境中的串类果实采摘机器人的低损收获奠定基础。
文摘[目的/意义]炭疽病(anthracnose)作为油茶生长过程中重要的病害,其严重程度的精准判定对于精准施药和科学管理具有重大意义。本研究提出了一种改进YOLACT(You Only Look At CoefficienTs)分级模型Camellia-YOLACT,旨在实现对油茶叶片炭疽病感染严重程度的自动、高效判定。[方法]首先在YOLACT主干网络部分使用Swin-Transformer来进行特征提取。Transformer架构的自注意力机制拥有全局感受野及移位窗口等特性,有效地增强了模型的特征提取能力;引入加权双向特征金字塔网络,融合不同尺度的特征信息,加强模型对不同尺度目标的检测能力,提高模型的检测精度;在激活函数的选择上,采用非线性能力更强的HardSwish激活函数替换原模型的ReLu激活函数。由于HardSwish在负值区域不是完全截断,对于输入数据中的噪声具有更高的鲁棒性,自然环境下的图像有着复杂的背景和前景信息,HardSwish的鲁棒性有助于模型更好地处理这些情况,进一步提升精度。[结果和讨论]采用迁移学习方式在油茶炭疽病感染严重程度分级数据集上进行实验验证。消融实验结果表明,本研究提出的Camellia-YOLACT模型的mAP75为86.8%,较改进前提升5.7%;mAPall为78.3%,较改进前提升2.5%;mAR为91.6%,较改进前提升7.9%。对比实验结果表明,Camellia-YOLACT在精度和速度方面表现均好于SOLO(Segmenting Objects by Locations),与Mask R-CNN算法相比,其检测速度提升了2倍。在室外的36组分级实验中进一步验证了Camellia-YOLACT模型的性能,其对油茶炭疽病严重程度的分级正确率达到了94.4%,K值平均绝对误差为1.09%。[结论]本研究提出的Camellia-YOLACT模型在油茶叶片和炭疽病病斑分割上具有较高的精度,能够实现对油茶炭疽病严重程度的自动分级,为油茶病害的精准防治提供技术支持,进一步推动油茶炭疽病诊断的自动化和智能化。
文摘玉米果穗的表型参数是玉米生长状态的重要表征,生长状况的好坏直接影响玉米产量和质量。为方便无人巡检机器人视觉系统高通量、自动化获取玉米表型参数,该研究基于YOLACT(you only look at coefficients)提出一种高精度-速度平衡的玉米果穗分割模型SwinT-YOLACT。首先使用Swin-Transformer作为模型主干特征提取网络,以提高模型的特征提取能力;然后在特征金字塔网络之前引入有效通道注意力机制,剔除冗余特征信息,以加强对关键特征的融合;最后使用平滑性更好的Mish激活函数替换模型原始激活函数Relu,使模型在保持原有速度的同时进一步提升精度。基于自建玉米果穗数据集训练和测试该模型,试验结果表明,SwinT-YOLACT的掩膜均值平均精度为79.43%,推理速度为35.44帧/s,相较于原始YOLACT和其改进算法YOLACT++,掩膜均值平均精度分别提升了3.51和3.38个百分点;相较于YOLACT、YOLACT++和Mask R-CNN模型,推理速度分别提升了3.39、2.58和28.64帧/s。该模型对玉米果穗有较为优秀的分割效果,适于部署在无人巡检机器人视觉系统上,为玉米生长状态监测提供技术支撑。
文摘基于深度学习的遥感图像检测在农业生产、军事打击等领域都有所应用。但深度学习模型有计算复杂度高和参数量大的问题,而实际部署深度模型的边缘设备计算性能有限。文章以高分辨率遥感图像作为研究对象,对单阶段的实例分割网络算法进行改进,在Yolact(You Only Look At CoefficienTs)网络的基础上提出一种融入注意力机制和可变形卷积的轻量级实例分割算法。使用NWPU VHR-10遥感图像数据集对所提算法进行性能评估,实验结果表明,该算法能在保持性能的情况下减少计算复杂度和参数量。
基金funded by Anhui Provincial Natural Science Foundation(No.2208085ME128)the Anhui University-Level Special Project of Anhui University of Science and Technology(No.XCZX2021-01)+1 种基金the Research and the Development Fund of the Institute of Environmental Friendly Materials and Occupational Health,Anhui University of Science and Technology(No.ALW2022YF06)Anhui Province New Era Education Quality Project(Graduate Education)(No.2022xscx073).
文摘The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.
基金supported by the Natural Science Foundation of Guizhou Province(Grant Number:20161054)Joint Natural Science Foundation of Guizhou Province(Grant Number:LH20177226)+1 种基金2017 Special Project of New Academic Talent Training and Innovation Exploration of Guizhou University(Grant Number:20175788)The National Natural Science Foundation of China under Grant No.12205062.
文摘Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems.The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information,which is more accurate and reliable than object detection.However,the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed.Therefore,this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT,which fuses the ICIoU(Improved Complete Intersection over Union)and Res2Net for the YOLACT algorithm.Specifically,the ICIoU function can effectively solve the degradation problem of the original CIoU loss function,and improve the training convergence speed and detection accuracy.The Res2Net module fused with the ECA(Efficient Channel Attention)Net is added to the model’s backbone network,which improves the multi-scale detection capability and mask prediction accuracy.Furthermore,the Cluster NMS(Non-Maximum Suppression)algorithm is introduced in the model’s bounding box regression to enhance the performance of detecting similarly occluded objects.The experimental results demonstrate the superiority of FIR-YOLACT to the based methods and the effectiveness of all components.The processing speed reaches 28 FPS,which meets the demands of real-time vehicle instance segmentation.
