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Defect Detection from Power Line images using Advanced Deep Detectors 13

Defect Detection from Power Line Images using Advanced Deep ...

引用

13th International conference on Wireless communications and Signal processing, WCSP 2021

作者： Wang, Hongxing Huang, Zheng Chen, Yuquan Zhang, Xin Shen, Jie Mao, Weiping Hao, Zhenyang Jiangsu Frontier Electric Power Technology Co. Ltd. Nanjing China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China

ISBN: (纸本)9781665407854

Automatic defect detection from power line images captured by unmanned aerial vehicles (UAV), e.g., drones, using deep learning is an important research topic with significant applications. In this paper, we first collect a large-scale high-resolution power line image dataset and manually annotate the defect bounding boxes. Then, we apply the state-of-the-art deep learning detectors, such as Faster R-CNN, YOLOv4 and FCOS, on this dataset. By applying suitable pre-processing and multi-crop training, we obtain impressive power line defect detection results and provide extensive analysis on the results. The research in this paper can provide a strong baseline for future studies on this challenging computer vision application problem. © 2021 ieee.

关键词： Defects

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Evaluating Attention in Convolutional Neural Networks for Blended images

Evaluating Attention in Convolutional Neural Networks for Bl...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Andrea Portscher Sebastian Stabinger Antonio Rodríguez-Sánchez Department of Computer Science University of Innsbruck Innsbruck Austria

ISBN: (纸本)9781665462204

In neuroscientific experiments, blended images are used to examine how attention mechanisms in the human brain work. They are particularly suited for this research area, as a subject needs to focus on particular features in an image to be able to classify superimposed objects. As Convolutional Neural Networks (CNNs) take some inspiration from the mammalian visual system – such as the hierarchical structure where different levels of abstraction are processed on different network layers – we examine how CNNs perform on this task. More specifically, we evaluate the performance of four popular CNN architectures (ResNet18, ResNet50, CORnet-Z, and Inception V3) on the classification of objects in blended images. Since humans can rather easily solve this task by applying object-based attention, we also augment all architectures with a multi-headed self-attention mechanism to examine its effect on performance. Lastly, we analyse if there is a correlation between the similarity of a network architecture's structure to the human visual system and its ability to correctly classify objects in blended images. Our findings showed that adding a self-attention mechanism reliably increases the similarity to the V4 area of the human ventral stream, an area where attention has a large influence on the processing of visual stimuli.

关键词： visualization Correlation image processing visual systems Streaming media Network architecture Convolutional neural networks

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Automatic Quantification of Plant Disease from Field image Data Using Deep Learning

Automatic Quantification of Plant Disease from Field Image D...

引用

ieee Winter conference on Applications of Computer Vision (WACV)

作者： Garg, Kanish Bhugra, Swati Lall, Brejesh Indian Inst Technol Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9780738142661

Plant disease is a major factor in yield reduction. Thus, plant breeders currently rely on selecting disease-resistant plant cultivars, which involves disease severity rating of a large variety of cultivars. Traditional visual screening of these cultivars is an error prone process, which necessitates the development of an automatic framework for disease quantification based on field-acquired images using unmanned aerial vehicles (UAVs) to augment the throughput. Since these images are impaired by complex backgrounds, uneven lighting, and densely overlapping leaves, state-of-the-art frameworks formulate the processing pipeline as a dichotomy problem (i.e. presence/absence of disease). However, additional information regarding accurate disease localization and quantification is crucial for breeders. This paper proposes a deep framework for simultaneous segmentation of individual leaf instances and corresponding diseased region using a unified feature map with a multi-task loss function for an end-to-end training. We test the framework on field maize dataset with Northern Leaf Blight (NLB) disease and the experimental results show a disease severity correlation of 73% with the manual ground truth data and run-time efficiency of 5fps.

关键词： Deep learning Training Location awareness image segmentation visualization Pipelines Manuals

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An image Sentence Generation Based on Deep Neural Network Using RCNN-LSTM Model 6

An Image Sentence Generation Based on Deep Neural Network Us...

引用

6th International conference on image Information processing, ICIIP 2021

作者： Satyanarayana Reddy, S. Sai Kumar, Ashwani Jyaram, M. Computer Science Engineering Sreyas Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9781665433617

image captioning refers to generating a sentence description by analyzing the image. The objective of image captioning is to automatically generate these captions for an image to gain a deeper knowledge by using deep learning algorithms. In this paper, an image sentence generation based on deep neural network using RCNN-LSTM model is proposed. In the proposed model an image is taken as input and generate sentence as an output by making use of natural language processing for describing the contents of the image. We have developed this model by consistently analyzing a deep neural networks and image sentence generation methodologies. The scheme uses image datasets and their sentence descriptions to train and test the model and have balance between language and visual data. This research paper uses Recurrent Convolutional Neural Networks (RCNN) a combination of recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). RNN is used to process language part and CNN is used to process image part for obtaining feature vectors. Additionally, Long Short Term Memory (LSTM) is used for textual sentence generation. In this model, RCNN works as an encoder to retrieve features for the images by making use of Keras VGG16 and LSTM works as a decoder to obtain textual sentences which describes the images. In our approach we have used Flickr-8k, Flickr30K and MSCOCO dataset to train the model. The model image sentence generation achieved a very good accuracy for generating captions. © 2021 ieee.

关键词： Deep neural networks

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Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation

Robust Low-Light Human Pose Estimation through Illumination-...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Feng Zhang Ze Li Xiatian Zhu Lei Chen Nanjing University of Posts and Telecommunications Nanjing China Surrey Institute for People-Centred Artificial Intelligence University of Surrey United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As critical visual details become obscured, the low visibility and high ISO noise in extremely low-light images pose a significant challenge to human pose estimation. Current methods fail to provide high-quality representations due to reliance on pixel-level enhancements that compromise semantics and the inability to effectively handle extreme low-light conditions for robust feature learning. In this work, we propose a frequency-based framework for low-light human pose estimation, rooted in the "divide-and-conquer" principle. Instead of uniformly enhancing the entire image, our method focuses on task-relevant information. By applying dynamic illumination correction to the low-frequency components and low-rank denoising to the high-frequency components, we effectively enhance both the semantic and texture information essential for accurate pose estimation. As a result, this targeted enhancement method results in robust, high-quality representations, significantly improving pose estimation performance. Extensive experiments demonstrating its superiority over state-of-the-art methods in various challenging low-light scenarios.

关键词： Representation learning visualization Frequency modulation ISO Pose estimation Semantics Noise reduction Noise Lighting Speech processing

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processing and visualization of Tomography Signal with AI: Decision Making on COVID-19 4

Processing and Visualization of Tomography Signal with AI: D...

引用

4th International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2022

作者： Ishkov, Denis O. Goryachkin, Boris S. Terekhov, Valery I. Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9781665414340

The coronavirus COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. As the name suggests, the virus is characterized by SARS (Severe acute respiratory syndrome). Five times more deadly than the influenza virus, COVID-19 infects the respiratory system, causing coughing, shortness of breath and death in severe cases. One of the most effective ways to diagnose the form and severity of the disease is X-ray images and computed tomography (CT) of the chest. To improve the efficiency of diagnosing infection with high accuracy, a method based on artificial intelligence is necessary. Gradient Weighted Class Activation Mapping (Grad-CAM) highlights important areas in images using deep learning heat map models. The dynamic anamorphosis method is considered as a promising tool for decision support. The method helps to activate the visual-figurative mechanisms of thinking of a decision-maker and to simplify the study of information. The article describes an algorithm for cognitive visualization of a two-dimensional image of the lungs by anamorphosis of the Grad-CAM heat map. Directions for further research are outlined. © 2022 ieee.

关键词： COVID-19

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Spatial-Frequency Information Interaction Diffusion for SAR Colorization

Spatial-Frequency Information Interaction Diffusion for SAR ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xupei Zhang Hanlin Qin Jingjing Li Jinni Geng Zihan Gao Yue Yu School of Optoelectronic Engineering Xidian University Xi’an China Xi’an Key Laboratory of Infrared Technology and System Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The inherent speckle noise and grayscale characteristics of synthetic aperture radar (SAR) images pose challenges to information perception and interpretation. To address this issue, we propose a novel conditional diffusion model with spatial-frequency information interaction for SAR colorization, namely SFI4SC. Specifically, we introduce a learnable wavelet transform module to obtain the global frequency information of the input SAR image and then take it as a guiding condition to assist the diffusion model for SAR colorization. Additionally, we improved the network structure for the diffusion model by designing and introducing a spatial and frequency information interaction module to achieve multidimensional information interaction for the network inputs, further enhancing perceptual details and geometric structures of the SAR colorization results. Experimental results in the real-world remote sensing dataset show that our approach can successfully enhance visual clarity and informational content while preserving the unique characteristics of the SAR images. The code will be made publicly available.

关键词： Wavelet transforms Measurement visualization Noise Speckle Diffusion models Radar polarimetry Speech processing Synthetic aperture radar Remote sensing

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Object Detection Model Based on Dual visual Pathway

Object Detection Model Based on Dual Visual Pathway

引用

International conference on Wireless communications and Signal processing (WCSP)

作者： Ruixuan Chen Xiao Yao Yifeng Zeng The College of IoT Engineering Hohai University Changzhou Jiangsu China

ISBN: (纸本)9781665450867

Object detection has made great progress with the development of deep learning. However, existing object detection algorithms still have limitations compared with human brain. In addition, the interpretability of the model becomes more and more inefficient with the increasing accuracy of the model. Most of the object detection models are considered as “black box” that cannot be explained. Considering that the visual information processing system is composed of two pathways spatially distributed in the ventral and dorsal sides of the cerebral cortex when the brain detects objects, this paper proposes object detection model based on dual visual pathway by simulating the process of detecting objects in the brain. Intra layer recursion and remote connections of different modules are added to the feedforward network to achieve feature extraction for image, since the ventral flow pathway is a recurrent network composed of loops and feedback. Experimental results carried out on PASCAL VOC show that the performance of object detection has been further improved, and the interpretability of the model has been enhanced.

关键词： Wireless communication Deep learning visualization Object detection Information processing visual systems Signal processing

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image Reconstruction and Facial Feature Extraction for Criminal Identification Using Machine Intelligence (Epic Vision)

Image Reconstruction and Facial Feature Extraction for Crimi...

引用

Contemporary Computing and communications (InC4), ieee International conference on

作者： Rakshika S Shravya U Saieesh S Rao Nithanth Praveen Sawkar M. Sheela Devi Computer Science and Engineering PES University) Bengaluru Karnataka India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Criminal detection continues to face challenges when dealing with blurry, fragmented, or ambiguous visual information from surveillance sources. This paper is devoted to a sophisticated machine intelligence approach that attempts to address these issues. The main objective is to reconstruct and enhance the facial features and to carefully extract unique facial features. This approach is poised to provide more accurate and effective criminal detection methods. To achieve these goals, the project uses extensive facial datasets and state-of-the-art neural network models. By doing so, it seeks to change the way law enforcement agencies interpret and analyze physical evidence. The implemented system not only focuses on improving facial images but also ensures that vital facial landmarks are retained even under harsh conditions This modification of features a environmental image quality changes this is the main strength of the project, with real-world criminals -Promising to increase the efficiency of the investigation This study highlights the power of machine intelligence in law enforcement, and provides actionable insights that can reshape current practices His innovative facial reconstruction and feature extraction techniques are at the forefront of technological advances in this field. Moreover, the results of the project extend beyond the immediate context and have global significance. They have the potential to increase the efficiency and success of global security agencies and, ultimately, to help maintain public safety and preserve justice. This approach not only improves facial images but also ensures the retention of crucial facial landmarks, even in adverse conditions. By leveraging state-of-the-art machine intelligence, this project aims to significantly enhance criminal detection methods, offering tangible benefits to law enforcement agencies worldwide.

关键词： visualization Law enforcement Surveillance Neural networks Feature extraction Public security Security

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Individual Source Camera Identification with Convolutional Neural Networks 1

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14th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Bernacki, Jaroslaw Costa, Kelton A. P. Scherer, Rafal Czestochowa Tech Univ Dept Intelligent Comp Syst Al Armii Krajowej 36 PL-42200 Czestochowa Poland Sao Paulo State Univ Dept Comp Sao Paulo Brazil

ISBN: (数字)9789811982347

ISBN: (纸本)9789811982330;9789811982347

In this paper we consider the issue of digital camera identification which matches the area of digital forensics. This problem is well-known in the literature and many algorithms based on camera's fingerprint have been proposed. However, one may find that there is a little number of methods providing a fast and accurate digital camera identification. This problem is especially observed in terms of today's digital cameras, producing images of big sizes. In this paper we discuss several existing approaches based on convolutional neural networks (CNN). We try to find out whether it is possible to speed up the process of learning the networks by the images. One of the findings include replacing the ReLU with SELU activation function. We experimentally show that using SELU speeds up significantly the process of learning. We also compare the identification accuracy of all considered methods. The experiments are held on extensive image dataset, consisting of many images coming from modern cameras.

关键词： Security image processing Imaging sensor identification Camera identification Digital forensics

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