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CurveNet:Curvature-Based Multitask Learning Deep Networks for 3D Object Recognition

IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1177-1187页

作者： A.A.M.Muzahid Wanggen Wan ferdous sohel Lianyao Wu Li Hou School of Communications and Information Engineering Institute of Smart CityShanghai UniversityShanghai 200444China Discipline of Information Technology Murdoch UniversityMurdoch WA 6150Australia School of Information Engineering Huangshan UniversityHuangshan 245041China

In computer vision fields,3D object recognition is one of the most important tasks for many real-world ***-dimensional convolutional neural networks(CNNs)have demonstrated their advantages in 3D object *** this paper,we propose to use the principal curvature directions of 3D objects(using a CAD model)to represent the geometric features as inputs for the 3D *** framework,namely CurveNet,learns perceptually relevant salient features and predicts object class *** directions incorporate complex surface information of a 3D object,which helps our framework to produce more precise and discriminative features for object *** learning is inspired by sharing features between two related tasks,where we consider pose classification as an auxiliary task to enable our CurveNet to better generalize object label *** results show that our proposed framework using curvature vectors performs better than voxels as an input for 3D object *** further improved the performance of CurveNet by combining two networks with both curvature direction and voxels of a 3D object as the inputs.A Cross-Stitch module was adopted to learn effective shared features across multiple *** evaluated our methods using three publicly available datasets and achieved competitive performance in the 3D object recognition task.

关键词： 3D shape analysis convolutional neural network DNNs object classification volumetric CNN

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Deep learning based classification of sheep behaviour from accelerometer data with imbalance

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Information Processing in Agriculture 2023年第3期10卷 377-390页

作者： Kirk E.Turner Andrew Thompson Ian Harris Mark Ferguson ferdous sohel Discipline of Information Technology Murdoch UniversityMurdochWA 6150Australia Centre for Crop and Food Innovation Food Futures InstituteMurdoch UniversityMurdochWA 6150Australia College of Science HealthEngineering and EducationMurdoch UniversityMurdochWA 6150Australia neXtgen Agri Saint MartinsChristchurch 8022New Zealand

Classification of sheep behaviour from a sequence of tri-axial accelerometer data has the potential to enhance sheep *** behaviour is inherently imbalanced(e.g.,more ruminating than walking)resulting in underperforming classification for the minority activities which hold *** works have not addressed class imbalance and use traditional machine learning techniques,e.g.,Random Forest(RF).We investigated Deep Learning(DL)models,namely,Long Short Term Memory(LSTM)and Bidirectional LSTM(BLSTM),appropriate for sequential data,from imbalanced *** data sets were collected in normal grazing conditions using jaw-mounted and earmounted *** to this study,alongside typical single classes,e.g.,walking,depending on the behaviours,data samples were labelled with compound classes,e.g.,walking_-*** number of steps a sheep performed in the observed 10 s time window was also recorded and incorporated in the *** designed several multi-class classification studies with imbalance being addressed using synthetic *** models achieved superior performance to traditional ML models,especially with augmented data(e.g.,4-Class+Steps:LSTM 88.0%,RF 82.5%).DL methods showed superior generalisability on unseen sheep(i.e.,F1-score:BLSTM 0.84,LSTM 0.83,RF 0.65).LSTM,BLSTM and RF achieved sub-millisecond average inference time,making them suitable for real-time *** results demonstrate the effectiveness of DL models for sheep behaviour classification in grazing *** results also demonstrate the DL techniques can generalise across different *** study presents a strong foundation of the development of such models for real-time animal monitoring.

关键词： Sheep behaviour classification Data synthesis Class imbalance Grazing sheep

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A survey of image-based computational learning techniques for frost detection in plants

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Information Processing in Agriculture 2023年第2期10卷 164-191页

作者： Sayma Shammi ferdous sohel Dean Diepeveen Sebastian Zander Michael G.K.Jones Discipline of Information Technology Murdoch UniversityMurdochWA 6150Australia Centre for Crop and Food Innovation Food Futures InstituteMurdoch UniversityMurdochWA 6150Australia Department of Primary Industries and Regional Development Western AustraliaSouth PerthWA 6151Australia

Frost damage is one of the major concerns for crop growers as it can impact the growth of the plants and hence,*** detection of frost can help farmers mitigating its *** the past,frost detection was a manual or visual ***-based techniques are increasingly being used to understand frost development in plants and automatic assessment of damage resulting from *** research presents a comprehensive survey of the state-of the-art methods applied to detect and analyse frost stress in *** identify three broad computational learning approaches i.e.,statistical,traditional machine learning and deep learning,applied to images to detect and analyse frost in *** propose a novel taxonomy to classify the existing studies based on several *** taxonomy has been developed to classify the major characteristics of a significant body of published *** this survey,we profile 80 relevant papers based on the proposed *** thoroughly analyse and discuss the techniques used in the various approaches,i.e.,data acquisition,data preparation,feature extraction,computational learning,and *** summarise the current challenges and discuss the opportunities for future research and development in this area including in-field advanced artificial intelligence systems for real-time frost monitoring.

关键词： Frost Cold stress Machine learning Image analysis Crop Plant

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