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Recognition on the working status of Acetes chinensis quota fishing vessels based on a 3d convolutional neural network

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FISHERIES RESEARCH 2022年 248卷 106226-106226页

作者： Wang, Shuxian Zhang, Shengmao Liu, Yang Zhang, Jiaze Sun, Yongwen Yang, Yuhao Hu, Huijuan Xiong, Ying Fan, Wei Wang, Fei Tang, Fenghua Dalian Ocean Univ Sch Nav & Naval Architecture Dalian 116023 Peoples R China Chinese Acad Fishery Sci East China Sea Fisheries Res Inst Shanghai 200090 Peoples R China Jiangsu Marine Fisheries Res Inst Nantong 226007 Peoples R China Shanghai Ocean Univ Coll Informat Technol Shanghai 200090 Peoples R China

In the present study, a method for identifying the status of Acetes chinensis fishing vessels based on a 3d convolutional neural network is proposed, so as to protect marine biodiversity, monitor the working status of Acetes chinensis fishing vessels and assist in the realization of quota fishing. The Vessel Monitoring System (VMS) was installed on the quota fishing vessels to collect work data from June 16, 2021 to July 13, 2021. According to the characteristics of the fishing vessels, the work status of the fishing vessels was divided into five statuses such as stopping, sailing, putting net, waiting and pulling net. The 3d convolutional neural network Acetes3dNet was designed to extract the multi-dimensional and multi-level features of the data and trained in the training set. Finally, the effectiveness of the model was verified in the validation set. The training results were combined with the Beidou ship position data to restore the working process of the fishing vessel. The experimental results reveal that after 150 epochs of training, the precision, recall, and f1 score of Acetes3dNet on the training set reached 99.02%, 99.19%, and 99.09%, respectively, while the precision, recall, and f1 score on the validation set reached 97.09%, 96.82%, and 96.68%. Research shows that Acetes3dNet can circumvent the limitations of traditional 2d neural networks in dynamic target detection, complete recognition of the working status of Acetes chinensis quota fishing vessels, and show the historical work process of the ship in an intuitive manner. The experimental results are conducive to standardizing the management of fishing vessels and protecting marine life.

关键词： Acetes chinensis Quota fishing Fishing vessels status recognition 3d convolutional neural network

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Smoke Video detection Algorithm Based On 3d convolutional neural network

Smoke Video Detection Algorithm Based On 3D Convolutional Ne...

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第34届中国控制与决策会议

作者： Zhen Shi Rui Sun Mingge Huo Institute of Intelligent Systems Science and Engineering Harbin Engineering University Beijing Electromechanical Engineering Research Institute

ISBN: (纸本)9781665478977

Smoke detection can play a key role in the early stage of a *** video smoke detection mainly extracts image features of smoke and uses machine learning methods to classify and recognize ***,smoke video is an image of continuous frames,and adjacent frames of images are related to each *** time complexity is ignored and only spatial features are considered,the key features cannot be fully *** combining the 3 d convolutional neural network and introducing the attention mechanism,the temporal and spatial features of the smoke can be effectively weighted and merged,and the 3×1×1 and 1×3×3 convolution kernels are used instead of the 3×3×3 convolution *** model *** order to eliminate most of the non-smoke areas to reduce the time complexity of the system,the MobileNetV2-SSd algorithm is used to do a priori score to locate the suspected smoke *** results show that compared to traditional smoke detection methods and 2 d smoke detection convolutional networks,the accuracy of smoke detection has been improved.

关键词： Smoke video detection 3d convolutional neural network Temporal and spatial characteristics Attention mechanism Mobilenetv2-SSd

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Automatic lung nodule detection using a 3d deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs

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COMPUTERS IN BIOLOGY ANd MEdICINE 2018年 103卷 220-231页

作者： Gu, Yu Lu, Xiaoqi Yang, Lidong Zhang, Baohua Yu, dahua Zhao, Ying Gao, Lixin Wu, Liang Zhou, Tao Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Inner Mongolia Univ Sci & Technol Inner Mongolia Key Lab Pattern Recognit & Intelli Sch Informat Engn Baotou 014010 Peoples R China Inner Mongolia Univ Sci & Technol Sch Foreign Languages Baotou 014010 Peoples R China

Objective: A novel computer-aided detection (CAd) scheme for lung nodule detection using a 3d deep convolutional neural network combined with a multi-scale prediction strategy is proposed to assist radiologists by providing a second opinion on accurate lung nodule detection, which is a crucial step in early diagnosis of lung cancer. Method: A 3d deep convolutional neural network (CNN) with multi-scale prediction was used to detect lung nodules after the lungs were segmented from chest CT scans, with a comprehensive method utilized. Compared with a 2d CNN, a 3d CNN can utilize richer spatial 3d contextual information and generate more discriminative features after being trained with 3d samples to fully represent lung nodules. Furthermore, a multi-scale lung nodule prediction strategy, including multi-scale cube prediction and cube clustering, is also proposed to detect extremely small nodules. Result The proposed method was evaluated on 888 thin-slice scans with 1186 nodules in the LUNA16 database. All results were obtained via 10-fold cross-validation. Three options of the proposed scheme are provided for selection according to the actual needs. The sensitivity of the proposed scheme with the primary option reached 87.94% and 92.93% at one and four false positives per scan, respectively. Meanwhile, the competition performance metric (CPM) score is very satisfying (0.7967). Conclusion: The experimental results demonstrate the outstanding detection performance of the proposed nodule detection scheme. In addition, the proposed scheme can be extended to other medical image recognition fields.

关键词： Lung nodule detection 3d convolutional neural network Multi-scale cube prediction Cube clustering deep learning

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Maximizing Accuracy of Fall detection and Alert Systems Based on 3d convolutional neural network: Poster Abstract 17

Maximizing Accuracy of Fall Detection and Alert Systems Base...

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Proceedings of the Second International Conference on Internet-of-Things design and Implementation

作者： Seokhyun Hwang daeHan Ahn Homin Park Taejoon Park Hanyang University Daegu Gyeongbuk Institute of Science and Technology

ISBN: (纸本)9781450349666

We present a deep-learning-based approach to maximize the accuracy and reliability of vision-based fall detection and alert systems. The proposed approach utilizes a 3d convolutional neural network (3d-CNN) to analyze the continuous motion data obtained from depth cameras and exploits a data augmentation method to do away with overfitting. Our preliminary evaluation results demonstrate that it achieves the classification accuracy of up to 96.9%.

关键词： 3d convolutional neural network elderly care deep learning IoT applications fall detection

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3d deep convolutional neural network for predicting neurosensory retinal thickness map from spectral domain optical coherence tomography volumes 14

3D Deep Convolutional Neural Network for predicting neurosen...

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14th International Symposium on Medical Information Processing and Analysis

作者： Perdomo, Oscar J. Rios, Hernan A. Rodriguez, Francisco J. Gonzalez, Fabio A. Univ Nacl Colombia MindLab Res Grp Bogota Colombia Fdn Oftalmol Nacl Bogota Colombia

ISBN: (数字)9781510626065

ISBN: (纸本)9781510626065

Age-related macular degeneration is a common cause of vision loss in people aging 55 and older. The condition affects the light-sensing cells in the macula limiting the sharp and central vision. On the other hand, Spectral domain Optical Coherence Tomography (Sd-OCT) allow highlighting abnormalities and thickness in the retinal layers which are useful for age-related macular degeneration diagnosis and follow up. The Neurosensory retina (NSR) map is defined as the thickness between the inner limiting membrane layer and the inner aspect of the retinal pigment epithelium complex. Additionally, the NSR map has been used to differentiate between healthy and subjects with macular problems, but the plotting of the retinal thickness map depends critically on additional manufacturer interpretation software to automatically drawing. Therefore, this paper presents an end-to-end 3d convolutional neural network to automatically extract nine thickness mean values to draw the NSR map from an Sd-OCT.

关键词： Age-related macular degeneration neurosensory retina 3d convolutional neural network Sd-OCT retinal thickness map

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Video Super-resolution by Generative Adversarial network with 3d convolutional neural networks

Video Super-resolution by Generative Adversarial Network wit...

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International Workshop on Advanced Imaging Technology (IWAIT)

作者： Moriyama, Kohei Ono, Naoki Inoue, Kohei Hara, Kenji Kyushu Univ Fac Design 4-9-1 Shiobaru Minami Ku Fukuoka 8158540 Japan

ISBN: (纸本)9781510663084;9781510663091

For effective super-resolution processing of video images, video images should not be processed as frame -by -frame twodimensional information, but as spatio-temporal information, including information in the time axis direction. Most of the proposed video super-resolution processing based on deep learning uses 2d convolutional neural networks (CNNs). Therefore, the system is based on the 2d CNNs with the additional processing related to change from frame to frame. Instead of processing video images separately in space and time, comprehensive processing as spatio-temporal information can be expected to be more flexible and effective. In this research, we propose a video super-resolution process that processes spatio-temporal information by 3d-CNNs and GAN (Generative adversarial networks). By using 3d-CNNs, the configuration does not require motion alignment as preprocessing. Since the essential purpose of the super-resolution process is to predict missing high-frequency components, we added a process that directly predicts the difference between the high-resolution image and the corresponding bicubic interpolated low-resolution image.

关键词： super-resolution 3d convolutional neural network generative adversarial networks spatio-temporal information

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Unlocking the Power of 3d convolutional neural networks for COVId-19 detection: A Comprehensive Review

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JOURNAL OF IMAGING INFORMATICS IN MEdICINE 2025年 1-19页

作者： Ilesanmi, Ademola E. Ilesanmi, Taiwo Ajayi, Babatunde Gbotoso, Gbenga A. Belhaouari, Samir Brahim Univ Penn Philadelphia PA 19104 USA Natl Populat Commiss Abuja Nigeria King Prajadhipoks Inst Bangkok 10210 Thailand Hamad Bin Khalifa Univ Coll Sci & Engn Ar Rayyan Qatar Lagos State Univ Sci & Technol Ikorodu Nigeria

The advent of three-dimensional convolutional neural networks (3d CNNs) has revolutionized the detection and analysis of COVId-19 cases. As imaging technologies have advanced, 3d CNNs have emerged as a powerful tool for segmenting and classifying COVId-19 in medical images. These networks have demonstrated both high accuracy and rapid detection capabilities, making them crucial for effective COVId-19 diagnostics. This study offers a thorough review of various 3d CNN algorithms, evaluating their efficacy in segmenting and classifying COVId-19 across a range of medical imaging modalities. This review systematically examines recent advancements in 3d CNN methodologies. The process involved a comprehensive screening of abstracts and titles to ensure relevance, followed by a meticulous selection and analysis of research papers from academic repositories. The study evaluates these papers based on specific criteria and provides detailed insights into the network architectures and algorithms used for COVId-19 detection. The review reveals significant trends in the use of 3d CNNs for COVId-19 segmentation and classification. It highlights key findings, including the diverse range of networks employed for COVId-19 detection compared to other diseases, which predominantly utilize encoder/decoder frameworks. The study provides an in-depth analysis of these methods, discussing their strengths, limitations, and potential areas for future research. The study reviewed a total of 60 papers published across various repositories, including Springer and Elsevier. The insights from this study have implications for clinical diagnosis and treatment strategies. despite some limitations, the accuracy and efficiency of 3d CNN algorithms underscore their potential for advancing medical image segmentation and classification. The findings suggest that 3d CNNs could significantly enhance the detection and management of COVId-19, contributing to improved healthcare outcomes.

关键词： 3d convolutional neural network Medical images Segmentation and classification Computed Tomography (CT) COVId-19

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2dSlicesNet: A 2d Slice-Based convolutional neural network for 3d Object Retrieval and Classification

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IEEE ACCESS 2021年 9卷 24041-24049页

作者： Taybi, Ilyass Ouazzani Gadi, Taoufiq Alaoui, Rachid Hassan First Univ Fac Sci & Tech LIIMSC Lab Settat 26000 Morocco Mohammed V Univ Fac Sci LRIT Lab Rabat 10000 Morocco Mohammed V Univ Higher Sch Technol Sale LASTIMI Lab Rabat 10000 Morocco

3d data can be instrumental to the computer vision field as it provides insightful information about the full 3d models' geometry. Recently, with easy access to both computational power and huge 3d databases, it is feasible to apply convolutional neural networks to automatically extract the 3d models' features. This paper presents a novel approach, called 2dSlicesNet, which deals with the issue of 3d model retrieval and classification using a 2d slice-based representation with a 3d convolutional neural network. The assumption in this context is that similar 3d models will be composed of almost identical 2d slices. Therefore, we first transform each normalized 3d model into a set of 2d slices corresponding to its first main axis, and then use them as input data to our 3d convolutional neural network. Experimental results and comparison with state-of-the-art approaches, using ModelNet10 and ModelNet40 datasets, prove that our proposed 2dSlicesNet approach can reach notable rates of accuracy in classification and retrieval.

关键词： Three-dimensional displays Two dimensional displays Solid modeling Convolution Computational modeling Feature extraction convolutional neural networks deep learning 2d slices 3d convolutional neural network 3d object classification 3d object retrieval

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3d convolutional neural networks to Estimate Assembly Process Parameters using 3d Point-Clouds

3D Convolutional Neural Networks to Estimate Assembly Proces...

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Conference on Multimodal Sensing - Technologies and Applications

作者： Sinha, Sumit Glorieux, Emile Franciosa, Pasquale Ceglarek, dariusz Univ Warwick WMG DLM Coventry CV4 7AL W Midlands England

ISBN: (数字)9781510627987

ISBN: (纸本)9781510627987

Closed loop dimensional quality control for an assembly system entails controlling process parameters based on dimensional quality measurement data to ensure that products conform to quality requirements. Effective closed-loop quality control reduces machine downtime and increases productivity, as well as enables efficient predictive maintenance and continuous improvement of product quality. Accurate estimation of dimensional variations on the final part is a key requirement, in order to detect and correct process faults, for effective closed-loop quality control. Nowadays, this is often done by experienced process engineers, using a trial-and-error approach, which is time-consuming and can be unreliable. In this paper, a novel model to estimate process parameters error variations using high-density cloud-of-point measurement data captured by 3d optical scanners is proposed. The proposed model termed as PointdevNet uses 3d convolutional neural networks (CNN) that leverage the deviations of key nodes and their local neighbourhood to estimate the process parameter variations. These process parameters variation estimates are leveraged for root cause isolation as a necessary but currently missing step needed for the development of closed-loop quality control framework. The proposed model is compared with an existing state-of-the-art linear model under different scenarios such as a single and multiple root causes, and the presence of measurement noise. The state-of-the-art model is evaluated under different point selections and results are compared to the proposed model with consideration to an industrial case study involving a sheet metal part, i.e. window reinforcement panel.

关键词： 3d convolutional neural network deep Learning 3d Optical Scanner Point-Clouds Multi-Wave Light Technology Closed-Loop Quality Control Root Cause Analysis Artificial intelligence

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diagnosis of pulmonary tuberculosis with 3d neural network based on multi-scale attention mechanism

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MEdICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第5期62卷 1589-1600页

作者： Zhang, Shidong He, Cong Wan, Zhenzhen Shi, Ning Wang, Bing Liu, Xiuling Hou, dailun Hebei Univ Coll Elect & informat Engn Key Lab Digital Med Engn Hebei Prov Baoding 071002 Peoples R China Capital Med Univ Beijing Chest Hosp Dept Radiol Beijing 101149 Peoples R China

This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3d) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3d CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions.

关键词： Tuberculosis CT images Attention mechanism deep learning 3d convolutional neural network Multi-scale input

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