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FCOS-LSC:A Novel Model for Green Fruit Detection in a Complex Orchard Environment

Plant Phenomics 2023年第3期5卷 546-563页

作者： Ruina Zhao Yujie Guan Yuqi Lu Ze Ji Xiang Yin weikuan jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Engineering Cardiff UniversityCardiff CF243AAUK School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013China

To better address the difficulties in designing green fruit recognition techniques in machine vision systems,a new fruit detection model is *** model is an optimization of the FCOS(full convolution one-stage object detection)algorithm,incorporating LSC(level scales,spaces,channels)attention blocks in the network structure,and named *** method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions,lighting conditions,and capture ***,the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature *** feature pyramid network(FPN)is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected ***,the attention mechanisms are added to each of the 3 dimensions of scale,space(including the height and width of the feature map),and channel of the generated multiscale feature map to improve the feature perception capability of the ***,the classification and regression subnetworks of the model are applied to predict the fruit category and bounding *** the classification branch,a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit *** proposed FCOS-LSC model has 38.65M parameters,38.72G floating point operations,and mean average precision of 63.0%and 75.2%for detecting green apples and green persimmons,*** summary,FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural ***,FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.

关键词： connected designing weights

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Novel green-fruit detection algorithm based on D2D framework

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 251-259,F0003页

作者： Jinmeng Wei Yanhui Ding Jie Liu Muhammad Zakir Ullah Xiang Yin weikuan jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000ShandongChina Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013JiangsuChina

In the complex orchard environment,the efficient and accurate detection of object fruit is the basic requirement to realize the orchard yield measurement and automatic *** it is hard to differentiate between the object fruits and the background because of the similar color,and it is challenging due to the ambient light and camera angle by which the photos have been *** problems make it hard to detect green fruits in orchard *** this study,a two-stage dense to detection framework(D2D)was proposed to detect green fruits in orchard *** proposed model was based on multi-scale feature extraction of target fruit by using feature pyramid networks MobileNetV2+FPN structure and generated region proposal of target fruit by using Region Proposal Network(RPN)*** the regression branch,the offset of each local feature was calculated,and the positive and negative samples of the region proposals were predicted by a binary mask prediction to reduce the interference of the background to the prediction *** the classification branch,features were extracted from each sub-region of the region proposal,and features with distinguishing information were obtained through adaptive weighted pooling to achieve accurate *** new proposed model adopted an anchor-free frame design,which improves the generalization ability,makes the model more robust,and reduces the storage *** experimental results of persimmon and green apple datasets show that the new model has the best detection performance,which can provide theoretical reference for other green object detection.

关键词： green-fruit detection D2D framework automatic harvesting MobileNetV2+FPN binary mask prediction anchor-free

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BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment

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Plant Phenomics 2022年第1期4卷 75-93页

作者： Meili Sun Liancheng Xu Xiude Chen Ze Ji Yuanjie Zheng weikuan jia School of Information Science and Engineering Shandong Normal UniversityJinanChina Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Zhenjiang 212013China National Engineering Research Center for Apple Shandong Agriculture UniversityTaian 271018China School of Engineering Cardiff UniversityCardiff CF243AAUK

Despite of significant achievements made in the detection of target fruits,small fruit detection remains a great challenge,especially for immature small green fruits with a few *** closeness of color between the fruit skin and the background greatly increases the difficulty of locating small target fruits in the natural orchard *** this paper,we propose a balanced feature pyramid network(BFP Net)for small apple detection.

关键词： Net pyramid locating

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ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

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Computer Systems Science & Engineering 2021年第3期36卷 537-549页

作者： Yanyun jiang Yuanjie Zheng Xiaodan Sui Wanzhen jiao Yunlong He weikuan jia School of Information Science and Engineering Shandong Normal UniversityJinan250358China Key Laboratory of Intelligent Computing&Information Security in Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software TechnologyInstitute of Biomedical SciencesShandong Normal UniversityJinan250358China Shandong Provincial Hospital Affiliated to Shandong University Jinan250021China INSA Lyon University of LyonCNRSInsermVilleurbanne69621CedexFrance

Multispectral imaging (MSI) technique is often used to capture imagesof the fundus by illuminating it with different wavelengths of light. However,these images are taken at different points in time such that eyeball movementscan cause misalignment between consecutive images. The multispectral imagesequence reveals important information in the form of retinal and choroidal bloodvessel maps, which can help ophthalmologists to analyze the morphology of theseblood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deeplearning framework called “Adversarial Segmentation and Registration Nets”(ASRNet) for the simultaneous estimation of the blood vessel segmentation andthe registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills theblood vessel segmentation task, and (ii) A registration module R that estimatesthe spatial correspondence of an image pair. Based on the segmention-drivenregistration network, we train the segmentation network using a semi-supervisedadversarial learning strategy. Our experimental results show that the proposedASRNet can achieve state-of-the-art accuracy in segmentation and registrationtasks performed with real MSI datasets.

关键词： Deep learning deformable image registration image segmentation multispectral imaging(MSI)

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Joint Deep Matching Model of OCT Retinal Layer Segmentation

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Computers, Materials & Continua 2020年第6期63卷 1485-1498页

作者： Mei Yang Yuanjie Zheng weikuan jia Yunlong He Tongtong Che Jinyu Cong School of Information Science and Engineering at Shandong Normal University Jinan250358China Key Lab of Intelligent Computing and Information Security in Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software TechnologyInstitute of Biomedical SciencesShandong Normal UniversityJinan250358China INSA Lyon University of LyonCNRSInsermVilleurbanne69621 CedexFrance

Optical Coherence Tomography(OCT)is very important in medicine and provide useful diagnostic *** retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular *** the existing retinal layer segmentation approaches,learning or deep learning-based methods belong to the ***,most of these techniques rely on manual-marked layers and the performances are limited due to the image *** order to overcome this limitation,we build a framework based on gray value curve matching,which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from *** depth convolution network learns the column correspondence in the OCT image *** whole OCT image participates in the depth convolution neural network operation,compares the gray value of each column,and matches the gray value sequence of the transformation column and the next *** this algorithm,when a boundary point is manually specified,we can accurately segment the boundary between retinal *** experimental results obtained from a 54-subjects database of both normal healthy eyes and affected eyes demonstrate the superior performances of our approach.

关键词： OCT retinal segmentation deep learning 1D convolution

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Preprocessing method of night vision image application in apple harvesting robot

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International Journal of Agricultural and Biological Engineering 2018年第2期11卷 158-163页

作者： weikuan jia Yuanjie Zheng De’an Zhao Xiang Yin Xiaoyang Liu Ruicheng Du School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Jiangsu UniversityZhenjiang 212013China School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000China Institute of Life Sciences and Key Lab of Intelligent Information Processing Shandong Normal UniversityJinan 250014China

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for *** machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation *** to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting *** different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be *** addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise *** results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper *** preprocessing method can provide a theoretical and technical reference for subsequent image processing.

关键词： apple harvesting robot night vision image preprocessing method color analysis noise analysis

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SE-COTR: A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard

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Plant Phenomics 2022年第1期4卷 12-25页

作者： Zhifen Wang Zhonghua Zhang Yuqi Lu Rong Luo Yi Niu Xinbo Yang Shaoxue Jing Chengzhi Ruan Yuanjie Zheng weikuan jia School of Information Science and Engineering Shandong Normal UniversityJinan 250358China State Key Laboratory of Biobased Materials and Green Papermaking Qilu University of Technology(Shandong Academy of Science)Jinan 25035China School of Informatics University of LeicesterLeicester LE17RHUK Department of Engineering Design and Mathematics University of the West of EnglandBristol BS161QYUK Fujian Key Laboratory of Intelligent Control and Manufacturing of Agricultural Machinery Wuyishan 354300China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Jiangsu UniversityZhenjiang 212013China

Because of the unstructured characteristics of natural orchards,the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent ***,an innovative fruit segmentation method based on deep learning,termed SE-COTR(segmentation based on coordinate transformer),is proposed to achieve accurate and real-time segmentation of green apples.

关键词： Application segmentation Complex

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苹果采摘机器人快速跟踪识别重叠果实

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农业工程学报 2015年第2期31卷 22-28页

作者：赵德安沈甜陈玉贾伟宽江苏大学电气信息工程学院镇江212013

为解决采摘机器人在运动状态下对重叠果实的识别问题,减少采摘过程处理的时间,对重叠果实的快速跟踪识别进行了研究。首先,对采集到的第1幅图像进行分割并去噪,之后通过计算圆内的点到轮廓边缘最小距离的极大值确定圆心的位置,计算圆心... 详细信息

为解决采摘机器人在运动状态下对重叠果实的识别问题,减少采摘过程处理的时间,对重叠果实的快速跟踪识别进行了研究。首先,对采集到的第1幅图像进行分割并去噪,之后通过计算圆内的点到轮廓边缘最小距离的极大值确定圆心的位置,计算圆心到轮廓边缘距离的最小值确定半径,通过圆心与半径截取后续匹配的模板,经试验证明该算法能较准确地找到重叠果实的圆心与半径。然后,确定连续采集的10幅图像的圆心,根据每幅图像圆心的位置对机器人的运动路径进行拟合、预判、综合半径与预判路径确定下一次图像处理的范围。最后,采用快速归一化互相关匹配对重叠果实进行匹配识别。试验证明,经过改进后的算法匹配识别时间与原算法相比,在没有进行预判的情况下匹配识别的时间为0.185 s,经过预判之后,匹配时间为0.133 s,减少了28.1%,采摘机器人的实时性得到了提高,能够满足实际需求。该研究可为苹果等类球形重叠果实的动态识别提供参考。

关键词：机器人果实图像处理重叠果实图像预判图像匹配

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苹果采摘机器人夜视图像的小波模糊阈值降噪

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华中科技大学学报（自然科学版） 2015年第S1期43卷 509-512 520,520页

作者：贾伟宽赵德安刘晓洋阮承治江苏大学电气信息工程学院江苏镇江212013

为实现苹果采摘机器人的全天候作业,对采集到的夜间图像进行了相关研究.在分析夜视图像噪声的基础上,运用小波阈值方法进行图像的降噪处理,针对阈值算法的潜在缺点,通过构造模糊阈值函数,提出小波模糊阈值的夜视图像降噪算法.实验结果表... 详细信息

为实现苹果采摘机器人的全天候作业,对采集到的夜间图像进行了相关研究.在分析夜视图像噪声的基础上,运用小波阈值方法进行图像的降噪处理,针对阈值算法的潜在缺点,通过构造模糊阈值函数,提出小波模糊阈值的夜视图像降噪算法.实验结果表明:从视觉上看小波模糊阈值降噪方法得到的低噪图像噪点明显减少;从客观数据比较,其相对峰值信噪比有较大幅度提高.新方法显示出在夜视图像降噪方面有着独特优势,为实现苹果采摘机器人的全天候作业打下基础.

关键词：采摘机器人夜视图像小波降噪模糊阈值人工光源峰值信噪比

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夜间低照度条件下苹果采摘机器人的图像识别

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华中科技大学学报（自然科学版） 2015年第S1期43卷 525-528页

作者：刘晓洋赵德安陈玉贾伟宽江苏大学电气信息工程学院江苏镇江212013

为提高苹果采摘机器人的环境适应能力,使其能够在夜间低照度人工光源条件下识别图像进行采摘,综合所采集图像的全部色彩信息和苹果表面像素点在RGB色彩空间中的聚集特性,在三维空间中首先用最小二乘拟合法对苹果表面典型样点进行空间直... 详细信息

为提高苹果采摘机器人的环境适应能力,使其能够在夜间低照度人工光源条件下识别图像进行采摘,综合所采集图像的全部色彩信息和苹果表面像素点在RGB色彩空间中的聚集特性,在三维空间中首先用最小二乘拟合法对苹果表面典型样点进行空间直线拟合,再依据样点到直线距离的均值和标准差建立阈值分割模型,初步识别夜间的图像.针对夜间图像中存在的阴影问题,采用相同的方法并结合阴影区域像素的二维位置信息对图像进行修正,实现图像识别的精确性和完整性.在光照范围内,不考虑过度遮挡,识别率可达90%以上,提高了夜间识别的精度和工作效率.

关键词：图像识别色彩空间苹果采摘最小二乘拟合三维空间阈值分割

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