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Identification of plants from the convolutional neural network

MULTIMEDIA TOOLS AND applications 2024年第23期83卷 63121-63131页

作者： Tiendrebeogo, Amed Changchun Univ Sci & Technol 7186 Weixing Rd Changchun Jilin Peoples R China

This paper studies a deep learning-based plant detection algorithm. Based on convolution neural network theory and digital image processing technology, 9 species of planes are detected. In order to further improve the accuracy of the detection algorithm, the paper proposes to improve the quality of the images. The experimental results show that the image quality is better after applying the following three algorithms the black channel prioritization algorithm, the quadratic combination algorithm of gray world and perfect reflection, and the contrast-limited adaptive histogram equalization algorithm, which is more conducive to the detection. Then, deep learning theory is applied to classify the plants. Comparing the classification results of AlexNet and GoogLeNet backbone networks, the accuracy of the jellyfish classification task based on GoogLeNet backbone network is 96.21%, which is better than AlexNet. Finally, the Faster R-CNN algorithm is used to detect plants and its detection performance is analyzed based on the two backbone networks mentioned above. The results show that the Faster R-CNN algorithm based on GoogLeNet has better detection accuracy in the jellyfish detection task, with an average detection accuracy of 74.96%.

关键词： Identification of plants image preprocessing artificial intelligence Convolutional neural network

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artificial neural networks in Agriculture, the core of artificial intelligence: What, When, and Why

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 230卷

作者： Castillo-Girones, Salvador Munera, Sandra Martinez-Sober, Marcelino Blasco, Jose Cubero, Sergio Gomez-Sanchis, Juan Inst Valenciano Invest Agr IVIA Ctr Agroingn CV-315Km 107 Moncada 46113 Valencia Spain Univ Politecn Valencia Dept Ingn Graf Camino Vera S-N Valencia 46022 Spain Univ Valencia Dept Ingn Elect IDAL Ave Univ S-N Burjassot 46100 Valencia Spain

artificial neural networks (ANNs) based models have emerged as a powerful tool for solving complex nonlinear problems in agriculture. These models simulate the human nervous system's structure, allowing them to learn hierarchical features from the data and solve nonlinear problems efficiently. Despite requiring a large amount of training data, ANNs with shallow architectures demonstrate superior performance in extracting relevant features and establishing accurate models, instilling confidence in their effectiveness compared to conventional machine learning methods. The versatility of ANNs enables their application in various agricultural domains, including precision agriculture, species classification, phenotyping, and food quality and safety assessment. ANNs combined with image analysis have proven valuable in disease detection, plant phenotyping, and fruit quality evaluation. The use of deep learning in agriculture has experienced exponential growth, as evident from the increasing number of publications in recent years. This article overviews recent advancements in applying ANNs in agriculture. It delves into the fundamental principles behind various types of agricultural data and ANN models, discussing their benefits and challenges. The article offers valuable insights into the proper use and functioning of each neural network, data processing for improved model outcomes, and the diverse applications of ANNs in the agricultural sector. It aims to equip readers with practical information on data utilisation, model selection based on data type, functionality, and current research applications.

关键词： Deep Learning neural networks applications Agricultural Products Quality

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Compression artifact reduction of low bit-rate videos via deep neural networks using self-similarity prior

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IET image processing 2023年第2期17卷 480-491页

作者： Liu, Dongsheng Yu, Runyi Chen, Wei-Gang Zhejiang Gongshang Univ Sch Comp & Informat Engn Hangzhou 0011 Peoples R China Eastern Mediterranean Univ Dept Elect & Elect Engn Gazimagusa Turkey

This paper presents a hybrid scheme that integratedly uses self-similarity prior and deep convolutional neural network (CNN) fusion for compression artifact reduction in low bit-rate video applications. Based on the temporal correlation hypothesis, the self-similarity prior is extended to the temporal domain by using as references not only the current decoded frame but also its neighbouring frames. Furthermore, being cognizant of that the bicubic downsampling process can typically improve the perceptual quality of a video coded at low bit-rate, for each small patch in the current frame, we search for similar patches in down-scaled versions of these references, and then form several self-similarity prior based predictions by tiling these similar patches at corresponding positions. To further exploit information flow across scales, a deep CNN model is constructed that contains two sub-networks to estimate the final output. One sub-network takes the self-similarity prior based predictions along with the decoded frame itself;and the other takes the down-scaled versions of these frames as network input. Experimental results demonstrate that the proposed method can remarkably improve, both subjectively and objectively, quality of compressed video sequences of low bit-rates.

关键词： low bit-rate video applications video signal processing neighbouring frames image and video coding neural nets video coding deep neural networks Optical, image and video signal processing Computer vision and image processing techniques convolutional neural nets current decoded frame self-similarity prior based predictions deep learning (artificial intelligence) deep CNN model data compression image sequences Video signal processing temporal domain low bit-rate videos image resolution network input compressed video sequences temporal correlation hypothesis compression artifact reduction

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Learning Visual Abstract Reasoning through Dual-Stream networks 38

Learning Visual Abstract Reasoning through Dual-Stream Netwo...

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38th AAAI conference on artificial Intelligence (AAAI) / 36th conference on Innovative applications of artificial Intelligence / 14th Symposium on Educational Advances in artificial Intelligence

作者： Zhao, Kai Xu, Chang Si, Bailu Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Chinese Inst Brain Res Beijing Peoples R China

ISBN: (纸本)1577358872

Visual abstract reasoning tasks present challenges for deep neural networks, exposing limitations in their capabilities. In this work, we present a neural network model that addresses the challenges posed by Raven's Progressive Matrices (RPM). Inspired by the two-stream hypothesis of visual processing, we introduce the Dual-stream Reasoning Network (DRNet), which utilizes two parallel branches to capture image features. On top of the two streams, a reasoning module first learns to merge the high-level features of the same image. Then, it employs a rule extractor to handle combinations involving the eight context images and each candidate image, extracting discrete abstract rules and utilizing an multilayer perceptron (MLP) to make predictions. Empirical results demonstrate that the proposed DRNet achieves stateof-the-art average performance across multiple RPM benchmarks. Furthermore, DRNet demonstrates robust generalization capabilities, even extending to various out-of-distribution scenarios. The dual streams within DRNet serve distinct functions by addressing local or spatial information. They are then integrated into the reasoning module, leveraging abstract rules to facilitate the execution of visual reasoning tasks. These findings indicate that the dual-stream architecture could play a crucial role in visual abstract reasoning.

关键词： Deep neural networks

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Diversified image style transfer-approaches, new methods and directed variability control

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MACHINE VISION AND applications 2025年第2期36卷 1-12页

作者： Ustyuzhanin, Alexander Kitov, Victor Kitov, Vladimir Yandex Technol LLC Artificial Intelligence & Res Dept 16 Lva Tolstogo St Moscow 119021 Russia Plekhanov Russian Univ Econ Artificial Intelligence Lab 36 Stremyanny Per Moscow 117997 Russia Lomonosov Moscow State Univ Fac Computat Math & Cybernet GSP-12nd Acad Bldg Moscow 119991 Russia

The task of image style transfer is to automatically redraw an input image in the style of another image, such as an artist's painting. The disadvantage of conventional stylization algorithms is the uniqueness of result. If the user is not satisfied with the way the style was transferred, he has no option to remake the stylization. The paper provides an overview of existing style transfer methods that generate diverse results after each run and proposes two new methods. The first method enables diversity by concatenating a random vector into inner image representation inside the neural network and by reweighting image features accordingly in the loss function. The second method allows diverse stylizations by passing the stylized image through orthogonal transformations, which impact the way the target style is transferred. These blocks are trained to replicate patterns from additional pattern images, which serve as additional input and provide an interpretable way to control stylization variability for the end user. Qualitative and quantitative comparisons demonstrate that both methods are capable to generate different stylizations with higher variability achieved by the second method. The code of both methods is available on github.

关键词： Artistic rendering Diversity image processing image generation neural networks

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Optimization Design of Building Network Structure Based on artificial Intelligence Algorithm and Data Set 2

Optimization Design of Building Network Structure Based on A...

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2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Ji, Xuanhao Nanjing Tech University Nanjing China

ISBN: (纸本)9798350360240

artificial intelligence algorithm is the key technology to realize the automated and intelligent design of building structures. However, due to the lack of constraints of physical rules, artificial intelligence algorithms sometimes generate unreasonable results in practical engineering applications. Therefore, by integrating the structural design rules into the Generative Adversarial Network (GAN) in the form of neural network modules, a new intelligent design method of building structure layout (FrameGAN-sym) is proposed. © 2024 IEEE.

关键词： Generative adversarial networks

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Dental caries diagnosis using neural networks and deep learning: a systematic review

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MULTIMEDIA TOOLS AND applications 2024年第10期83卷 30423-30466页

作者： Forouzeshfar, Parsa Safaei, Ali A. Ghaderi, Foad Kamangar, Sedighesadat Hashemi Kaviani, Hanieh Haghi, Sahebeh Tarbiat Modares Univ Fac Math Sci Dept Data Sci Tehran Iran Tarbiat Modares Univ Dept Med Informat Fac Med Sci Tehran Iran Tarbiat Modares Univ Fac Interdisciplinary Sci & Technol Dept Data Sci Tehran Iran Tarbiat Modares Univ Elect & Comp Engn Dept Human Comp Interact Lab Tehran Iran Univ Tehran Med Sci Sch Dent Restorat Dept Tehran Iran Univ Tehran Med Sci Sch Dent Dept Oral & Maxillofacial Radiol Tehran Iran Univ Tehran Med Sci Sch Dent Dept Operat Dent Tehran Iran

Dental caries is one of the oral health problems and the most common chronic infectious disease of childhood, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This review study aims to review studies published in the field of artificial intelligence and neural networks and dentistry. A search for studies in four databases, including Springer, ScienceDirect, PubMed (MedLine), and Institute of Electrical and Electronics Engineers (IEEE) was done. Finally, 28 studies were reviewed, most of which used Bitewing and Periapical images for the classification and detection of dental caries. The image databases ranged from 55 to 3000 and several evaluation metrics were used in the selected studies. The research questions were designed and reviewed based on PICOS (P stands for patient or problem, I stands for intervention, C stands for control or comparison, and O stands for outcomes). The majority of the studies also used pre-processing and data augmentation methods. The diversity between the networks used and the output evaluation criteria have made direct research comparisons challenging. The main focus of this research was on caries detection using deep learning methods and neural networks, especially convolutional neural networks that are suitable for images. The traditional methods of detecting caries, other than the methods based on artificial intelligence, have not been investigated in this research. Also, the main caries were interproximal and proximal caries in molars and premolars. The main difference between this and previous works is the use of more up-to-date articles (2016 to 2023) studies with an organized manner of reviewing, which is based on the types of images used.

关键词： Dental caries neural networks Convolutional neural networks (CNN) Diagnosis Detection Classification

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Performance Analysis of Deep CNN, YOLO, and LeNet for Handwritten Digit Classification 1

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International conference on artificial Intelligence: Theory and applications (AITA)

作者： Sarmah, Jibok Saini, Madan Lal Kumar, Ankush Chasta, Vidhan Chandigarh Univ Dept CSE Apex Inst Technol Mohali Punjab India

ISBN: (数字)9789819984794

ISBN: (纸本)9789819984787;9789819984794

The fundamental applications of handwritten digit classification are in the fields of optical reorganization of digits, bank check processing, recognizing zip codes on mail for postal, processing bank check amounts, and numeric entries in forms filled up by hand. For processing these types of tasks, different kinds of learning algorithms are used. A comparative study on performance analysis was done for convolutional neural network, LeNet-5, and YOLOv7. Publically available MNIST, DIDA, and MNIST MIX handwritten digit dataset were used in experimental work. The objective of this study is to find the best algorithm which can give an acceptable accuracy. To implement the model, this paper uses a deep neural network CNN and its architecture LeNet-5 and YOLOv7 which have become a potent tool for image categorization problems in recent years. This paper demonstrates the efficacy of deep learning approaches for effective and precise digit recognition, which can be expanded to numerous real-world applications needing accurate and dependable recognition of digits written on paper. This research has achieved the highest accuracy of 99.38% for LeNet-5.

关键词： Handwritten digit recognition Convolutional neural networks LeNet-5 YOLOv7 MNIST

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Deep learning for efficient high-resolution image processing: A systematic review

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INTELLIGENT SYSTEMS WITH applications 2025年 26卷

作者： Dede, Albert Nunoo-Mensah, Henry Tchao, Eric Tutu Agbemenu, Andrew Selasi Adjei, Prince Ebenezer Acheampong, Francisca Adoma Kponyo, Jerry John Kwame Nkrumah Univ Sci & Technol Dept Comp Engn Kumasi Ghana Kwame Nkrumah Univ Sci & Technol Responsible Artificial Intelligence Lab Kumasi Ghana Kumasi Ctr Collaborat Res Trop Med Global Hlth & Infect Dis Grp Kumasi Ghana Kwame Nkrumah Univ Sci & Technol Dept Telecommun Engn Kumasi Ghana

High-resolution images are increasingly used in fields such as remote sensing, medical imaging, and agriculture, but they present significant computational challenges when processed with deep learning models. This paper provides a systematic review of deep learning techniques developed to improve the efficiency of high-resolution image processing. We investigate techniques like lightweight neural networks, vision transformers adapted for high-resolution inputs, and models using frequency-domain inputs based on 96 studies from 2018 to 2023. These techniques have many applications, from environmental monitoring and urban planning to disease diagnosis. We emphasize the advancements in efficient high-resolution deep learning models, discussing their performance in terms of accuracy, speed, and resource requirements. Key challenges, including the trade-off between processing efficiency and model accuracy, are analysed, and potential future research directions are proposed to address these issues.

关键词： Deep learning High-resolution Computational efficiency image processing

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Synthesis of complex-valued InSAR data with a multi-task convolutional neural network

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2025年 220卷 192-206页

作者： Sibler, Philipp Sica, Francescopaolo Schmitt, Michael Univ Bundeswehr Munich Dept Aerosp Engn Werner Heisenberg Weg 39 D-85577 Neubiberg Germany Hensoldt Sensors GmbH Graf Von Soden Str D-88090 Immenstaad Germany

Simulated remote sensing images bear great potential for many applications in the field of Earth observation. They can be used as controlled testbed for the development of signal and image processing algorithms or can provide a means to get an impression of the potential of new sensor concepts. With the rise of deep learning, the synthesis of artificial remote sensing images by means of deep neural networks has become a hot research topic. While the generation of optical data is relatively straightforward, as it can rely on the use of established models from the computer vision community, the generation of synthetic aperture radar (SAR) data until now is still largely restricted to intensity images since the processing of complex-valued numbers by conventional neural networks poses significant challenges. With this work, we propose to circumvent these challenges by decomposing SAR interferograms into real-valued components. These components are then simultaneously synthesized by different branches of a multi-branch encoder-decoder network architecture. In the end, these real-valued components can be combined again into the final, complex-valued interferogram. Moreover, the effect of speckle and interferometric phase noise is replicated and applied to the synthesized interferometric data. Experimental results on both medium-resolution C-band repeat-pass SAR data and high-resolution X-band single-pass SAR data, demonstrate the general feasibility of the approach.

关键词： Synthetic aperture radar (SAR) Deep learning Multitask learning image synthesis SAR interferometry (InSAR)

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