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2024 10th International Conference on Applied Materials and Manufacturing Technology, ICAMMT 2024

作者： Liu, Zongxin Xie, Wenzhou Xiao, Muzheng Zhang, Zhijing Jin, Xin Qin, Yan School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Beijing Institute of Remote Sensing Equipment Beijing100048 China

This article discusses the servo control technology for the automatic screw-tightening process of a robotic arm based on multiple visual sensors, aiming at the assembly requirements of complex spatial structural components. The study emphasizes the importance of automated screw-tightening processes in industrial automation to improve efficiency and product quality. image processing and servo technology have been introduced to achieve precise control of the screw-tightening process. The research focuses on developing a distributed architecture to control robot arms and camera modules, process image information, and make servo adjustments based on perception data. Detailed introductions are given to the assembly control system model, visual observation scheme, image processing algorithms for hole recognition and screwdriver posture detection, as well as servo control mechanism. The research aims to improve the efficiency and accuracy of the screw-tightening process through advanced technology integration. © Published under licence by IOP Publishing Ltd.

关键词： Robotic arms

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image Pre-processing and Shadow Detection for Building Extraction from High-Resolution Satellite images 8

Image Pre-Processing and Shadow Detection for Building Extra...

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remote sensing Technologies and Applications in Urban Environments VIII 2023

作者： Prabhakar, Dolonchapa Garg, Pradeep Kumar Civil Engineering Department Indian Institute of Technology Uttarakhand Roorkee247667 India

ISBN: (纸本)9781510666993

High resolution satellite imageries frequently contain shadows because of high-rise structures, especially in urban areas. One of the notable flaws in remotely sensed imaging that prevents information extraction is shadow. Building detection depends on the recognition of shadowy areas since they indicate a building’s presence. Therefore, we can determine the structures’ shapes and heights by utilizing the sun’s illumination direction. But more crucially, detecting building shadows in high-resolution images can aid in better building detection. This work presents a shadow detection approach from a single RGB image to reduce the shadow effects on high-resolution images. We used LAB color space, which has one luminance (L) channel and two color channels (A and B). To generate the buffer zone for building extraction, we transformed the RGB images to the CIELab color space to acquire the shadow masks. The proposed algorithm must be pre-processed to separate each shadow area into a separate data matrix to expand its functionality, evaluate the effectiveness of the method, and produce more accurate results when building shadows are close to one another. Additionally, by grouping the shadow zones into independent matrices, the method enables the detection of two or more distinct shadows of buildings with complex geometries and many parts in separate zones. Pre-processing helps us to prepare the images for further analysis. Using image pre-processing wisely, one can offer advantages and address issues that ultimately improve local and global feature detection. The effectiveness of building extraction and the outcomes of image analysis both dramatically improved results by image pre-processing. © 2023 SPIE.

关键词： image analysis

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2023 5th International Symposium on Signal processing Systems, SSPS 2023

2023 5th International Symposium on Signal Processing System...

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5th International Symposium on Signal processing Systems, SSPS 2023

ISBN: (纸本)9798400700040

The proceedings contain 11 papers. The topics discussed include: accurate shadow height measurement technology of the SAR image;millimeter wave radar fall detection algorithm based on improved transformer;an end-to-end learning based covolutional neural network for single image defogging algorithm;ornaments and barlines recognition of numbered musical notation using YOLOv5;study on hyperspectral remote sensing images of GF-5 de-blurring based on sparse representation;design and implementation of target tracking system in low illumination environment based on FPGA;SAR image geometry correction technology based on block parallel signal processing;speech recognition method based on deep learning of artificial intelligence: an example of BLSTM-CTC model;and high precision reference measurement technology for mechanical scanning radar.

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Parallel classification and detection system for high-resolution remote sensing images 4

Parallel classification and detection system for high-resolu...

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4th International Conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Min, Jiang Xuebo, Zhang Chengguang, Zhang School of Space Information Space Engineering University Beijing China

ISBN: (纸本)9781665413220

In view of the characteristics of high score remote sensing data containing rich semantic information, combined with different processing requirements for high-score remote sensing images, this paper designs and implements a parallel classification and detection system based on high-score remote sensing image. With the development of remote sensing technology and artificial intelligence technology, it is of great significance to process high-score remote sensing image data based on deep learning convolution neural network. The system integrates sea land segmentation model and target detection model, which can realize different types of data processing requirements. It can realize the high-efficiency processing of large-scale images by means of parallel processing technology, and realize the detection effect of the specified targets by visual processing technology. It is applicable to the processing tasks of high-resolution remote sensing images of all types, and has a high level of intelligent and parallel processing. © 2021 IEEE.

关键词： remote sensing

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Research on remote sensing recognition of Forest Fire Smoke Based on Machine Learning

Research on Remote Sensing Recognition of Forest Fire Smoke ...

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International Conference on image processing, Computer Vision and Machine Learning (ICICML)

作者： Wang, Kaihua Pan, Jun Jiang, Lijun Sun, Yehan Wang, Kaisi Cao, Yu Jilin Univ Coll Geoexplorat Sci & Technol Changchun Peoples R China Heilongjiang Fundamental Geog Informat Ctr Nat Resources Dept Heilongjiang Prov Bur Surveying & Mapping Geog In Harbin Peoples R China

ISBN: (纸本)9781665464680

To investigate the differences in spectral features between smoke and other typical features, and to achieve remote sensing recognition of forest fire smoke. This paper takes muli county, liangshan, Sichuan province as the study area, uses Sentinel 2 multispectral images as the data source, extracts smoke, clouds, water bodies, vegetation, and bare ground image elements samples, builds forest fire smoke recognition models based on machine learning algorithms such as decision tree algorithm, support vector machine algorithm, and neural network algorithm, extracts the sum parameter of the reflection peak difference(SPRPD) and the product parameter of the reflection peak difference(PPRPD) to improve the models. The experimental results show that the recognition accuracies of the three models are 97.3%, 99.2%, and 99.4%, respectively, and the neural network model has the highest accuracy. The improved smoke recognition model reduces the probability of misclassification of thin clouds into smoke, and the model accuracies are improved to 99.0%,99.5%and 99.9%, respectively. The study shows that the combined feature parameters of the reflection peak band can effectively distinguish thin clouds and smoke, improve the accuracy of smoke recognition, and the smoke recognition model based on the neural network algorithm can effectively identify forest fire smoke.

关键词： smoke Sentinel-2 Neural network algorithm Characteristic parameters

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remote sensing Based Advance image Captioning Improved Feature Attention 7

Remote Sensing Based Advance Image Captioning Improved Featu...

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7th International Conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Sattar, Ammara Assam, Muhammad Alahmadi, Tahani Jaser Bhatti, Uzair Aslam Tang, Hao Aamir, Muhammad Harbin Engineering University Harbin China University of Science and Technology Department of Software Engineering KPK Bannu Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia School of Information and Communication Engineering Hainan University Haikou China College of Computer Science Huanggang Normal University Huanggang China

ISBN: (纸本)9798350350890

In the domain of image description generation, the focal point mechanism is crucial for highlighting the most relevant features within an image, thereby influencing the quality of generated descriptions. To address this challenge, we introduce an innovative optimization module called the Enhanced Focal (EF) mechanism, which builds upon the traditional multi-head focal point mechanism. The EF module is designed to assess the correlation levels between objects within an image, thereby guiding the process of subtitle generation more effectively. The EF mechanism integrates the output of the multi-head focal point mechanism with the current context (i.e., the query) to generate two components: an 'information vector' and an 'attention gate.' The 'attention gate' is then applied to the 'information vector' to create a refined focal point mechanism. This refined mechanism undergoes element-wise multiplication with the 'information vector,' producing attention information that reveals a strong correlation between the target and the background in the image. This process leverages the Long Short-Term Memory (LSTM) mechanism to enhance the model's capability to capture intricate relationships. Upon training and evaluating our enhanced model on the MS COCO dataset, we observed significant improvements in key evaluation metrics. The BLEU-1 and METEOR scores reached 73.01% and 28.81%, respectively, showcasing the effectiveness of the proposed EF mechanism in improving image description generation. © 2024 IEEE.

关键词： Data description

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MD-YOLOv8: A Multi-Object Detection Algorithm for remote sensing Satellite images

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IET image processing 2025年第1期19卷

作者： Zhang, Pengfei Liu, Jian Zhang, Jianqiang Liu, Yiping Li, Xingda College of Weaponry Engineering Naval University of Engineering Wuhan China

The technology for target recognition in remote sensing satellite images is widely applied in daily life, and research on detecting and recognizing targets in remote sensing images holds significant academic and practical importance. To address the challenges of extreme scale variations, dense target distributions, and low-resolution artefacts in remote sensing images, this paper proposes a new multi-object detection network based on the YOLOv8 architecture—MD-YOLOv8. The main contributions of this paper are threefold: (1) the design of the multi-frequency attention downsampling module, which integrates the ADown module with Haar wavelet transforms and pixel attention;(2) the proposal of the adaptive attention network (DMAA) module, an enhanced multiscale feature extractor based on the multiscale feature extraction attention mechanism;(3) the integration of both modules into the YOLOv8 backbone to achieve superior performance in remote sensing image detection. Based on the DOTA-1.0 dataset for training, experimental results show that the MD-YOLOv8 network achieves improvements in precision, recall rate, and mAP@0.5, reaching 82.69%, 78.28%, and 82.05%, respectively;these represent increases of 3.76%, 3.43%, and 4.37% compared to the original model. In practical image detection, MD-YOLOv8 demonstrates higher recognition quality and can flexibly respond to various target types. The MD-YOLOv8 network effectively meets the accuracy requirements for target detection in remote sensing satellite images. © 2025 The Author(s). IET image processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Photointerpretation

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Interpreting image texture metrics applied to landscape gradient data

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LANDSCAPE ECOLOGY 2023年第9期38卷 2179-2188页

作者： Riitters, Kurt Costanza, Jennifer K. Coulston, John W. Vogt, Peter Schleeweis, Karen USDA Forest Serv Southern Res Stn Asheville NC 28804 USA European Commiss Joint Res Ctr Ispra Italy USDA Forest Serv Rocky Mt Res Stn Ft Collins CO USA

Contextpattern metrics drawn from image processing and remote sensing have been applied as descriptors of the texture of landscape gradient data. Like some classical pattern metrics in ecology, texture has several facets which are measured by examining an adjacency matrix-the frequencies of co-occurring pixel values on a map-in different *** improve the interpretation and application of such metrics in landscape ecology we reformulate and interpret several of them by analogy to traditional metrics used with categorical *** and conclusions1. Four of the eight classical texture metrics measure attraction-the tendency for the same or similar values to be adjacent. Four others measure dispersion-the diversity of adjacencies relative to the entire adjacency matrix, the diagonal of the matrix, or the origin of the matrix. 2. The attraction metrics (dissimilarity, contrast, inverse difference, and homogeneity) differ only in the algebraic weights applied to different parts of an adjacency matrix. 3. The dispersion metrics (entropy, uniformity, difference entropy, and sum entropy) can be made more comparable by rescaling them to their maximum possible values. 4. While the metrics may be applied to any adjacency matrix, the choices about the method used to create an adjacency matrix have subtle yet important implications for the use and comparability of some metrics.

关键词： Landscape pattern Texture metrics Landscape gradient Grayscale data

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A NEW QUANTITATIVE EVALUATION STRATEGY FOR DEEP GENERATED SAR imageS

A NEW QUANTITATIVE EVALUATION STRATEGY FOR DEEP GENERATED SA...

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IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Yu, Ziyi Dong, Ganggang Xidian Univ Natl Lab Rader Signal Proc Xian Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Synthetic Aperture Radar Automatic Target recognition (SAR-ATR) is the core application of SAR technology. The shortage of training data is a constraint for SAR-ATR. One of the valid methods to solve the problem is SAR image simulation. A large quantity of generative models have achieved impressive performances on SAR image simulation. Therefore, it is absolutely necessary to evaluate whether the simulated images fulfill the requirement of application. This challenging problem has not attracted plenty of attention. Very few studies have been done. To fill the blank, we propose a new evaluation strategy. Two quantitative measurements are proposed to evaluate the simulated images from the local and the global perspective respectively. The local measurement, Frechet Inception Distance score (FID) is used to measure the distance of feature vector between the real images and the simulated images. Contrarily, the global measurement, Hybrid recognition Rate curve (HRR) is developed to assess the application capability of the simulated images from a global perspective. Multiple comparative experiments are performed on real SAR dataset. The experimental results demonstrate the effectiveness of the proposed method.

关键词： image quality evaluation SAR simulated images deep generative network

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Direct feature extraction and image co-registration of morphological structure from Bayer pattern raw planetary images

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartF期238卷

作者： Misra, Indranil Rohil, Mukesh Kumar Moorthi, S. Manthira Dhar, Debajyoti Indian Space Res Org ISRO Space Applicat Ctr Signal & Image Proc Area Ahmadabad Gujarat India Birla Inst Technol & Sci Dept Comp Sci & Informat Syst Pilani Rajasthan India

Feature extraction from planetary remote sensing images is a primary image processing task for object recognition, crater counter, morphological structure dimension and unpaired image co-registration. The paper presents a novel methodology to extract features directly from Bayer pattern raw planetary images. Gradient information is extracted from Bayer pattern image using standard edge operator that follows Color Difference Constancy (CDC) assumption. The proposed method's advantage is that it can skip the computationally intensive image processing pipeline and can have the Bayer pattern raw planetary image flow directly for useful information extraction. Sobel Edge Detector is applied on Indian Mars Color Camera (MCC) Bayer pattern raw images, and Gradient Magnitude Map (GMM) is generated at different Martian terrains. In addition, we have developed a direct image co-registration approach for MCC Bayer intensity raw image with respect to Mars Digital image Model (MDIM) 2.1 reference using Mode-Mean Combo Patch Filler and Gradient Intensity induced Scale Invariant Feature Transform (GI-SIFT) based feature matching. The outlier matched points are removed by Feature Similarity Score guided Random Sample Consensus (FSS-RANSAC) estimation technique. The visual evaluation and quantitative metrics indicate that GMM from MCC Bayer pattern raw image has negligible degradation with respect to GMM extracted from MCC demosaic image. The Root Mean Square Error (RMSE) is computed at different Mars regions, and it is found that the average image co-registration accuracy is less than 0.5 pixel.

关键词： Feature extraction Color difference constancy GI-SIFT FSS-RANSAC Bayer pattern image Mars color camera

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