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CNN-based On-board Intelligent processing for Remote Sensing images Interpretation 2

CNN-based On-board Intelligent Processing for Remote Sensing...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Cui, Xiaojie Liu, Yuanyuan Chen, Xuehua Wang, Gang Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9798350331417

Traditional remote sensing image processing is not able to provide timely information for near real-time applications due to the hysteresis of satellite-ground mutual communication and low processing efficiency. On-board intelligent processing is an important approach to improve the efficiency and intelligence of remote sensing satellites. This paper takes convolutional neural network (CNN) based on-board processing as the focus. Firstly, the basic workflow of CNN based on-board processing system is illustrated. Afterwards, the applications of lightweight CNN based on-board processing are thoroughly reviewed. The used CNN models are further analyzed to compare the advantages and disadvantages. Finally, current challenges are summarized and future works concerned with artificial intelligence are concluded. © 2023 IEEE.

关键词： change detection cloud detection earth observation satellites object detection on-board intelligent processing

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machine vision based autonomous cruise inspection vehicle hole recognition and positioning system 3

Machine vision based autonomous cruise inspection vehicle ho...

引用

3rd International conference on Energy and Electrical Power Systems (ICEEPS)

作者： Niu, Shuofeng Wu, Yanda Zhao, Yafeng Li, Zhen Zhao, Qiwei Xu, Wanshu Liu, Yingnan State Grid Shandong Elect Power Co Heze Power Supply Co Substn Operat & Maintenance Ctr Heze Peoples R China Shandong Univ Sch Elect Engn Jinan Peoples R China

ISBN: (纸本)9798350375145;9798350375138

A machine vision based autonomous cruise inspection car hole recognition and positioning system is designed to address the issues of low operating efficiency, high labor intensity, and potential safety accidents in high-voltage switchgear maintenance cars. Using a visual system to take photos of the fixed holes on the switchgear, image filtering, hole processing, circular fitting, and other processing are performed on the collected images to obtain the position information of the holes. Combined with the algorithm for calculating the angle between the positioning holes on the switchgear and the algorithm for coordinate positioning of the holes, the rotation angle and displacement of the motor control system are obtained. The experiment was conducted using a circular hole with a diameter of 8mm, and the results showed that the accuracy of hole alignment and positioning reached 100%, which can meet the automated detection requirements of hole recognition and alignment for maintenance vehicles.

关键词： machine vision maintenance car hole position recognition autonomous cruise control

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Research on Intelligent System of Complex Structured Light Three-Dimensional Measurement Based on machine vision 4

Research on Intelligent System of Complex Structured Light T...

引用

4th IEEE International conference on Data Science and Computer Application, ICDSCA 2024

作者： Xu, Zhihua Xu, Wenquan School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350368239

This system integrates complex structured light projection and machine vision imaging technology to achieve efficient and accurate measurement of three-dimensional structures. In order to deal with the high computational load and error problems of traditional methods in data processing, this paper designs an image processing solution based on compressed sensing algorithm. By means of compressed sensing technology, the quantity of data is effectively decreased during the process of image collection and data reconstruction, thus remarkably lessening the computational load. This approach not only streamlines the data-processing procedure but also enhances the measurement precision of the system on complex surfaces, allowing the system to attain a better equilibrium between real-time performance and accuracy. To confirm the performance and superiority of the algorithm, this paper builds multiple test models in a simulation environment and comprehensively assesses the impact of the compressed sensing algorithm by contrasting the measurement results under different structured light modes. The experimental outcomes demonstrate that in comparison with the traditional method, the proposed algorithm exhibits remarkable advantages in handling complex curved surfaces and unevenly reflecting surfaces. It can keep low inaccuracies while guaranteeing high precision and has strong sturdiness against external interference. Specific data show that the measurement error of the system under complex surface conditions is controlled within 0.05 mm, meeting the needs of high-precision measurement. ©2024 IEEE.

关键词： complex structure compressed sensing algorithm machine vision optical 3D measurement

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Visual recognition and comparison system and method of intelligent watt hour meter chip based on convolutional neural network

Visual recognition and comparison system and method of intel...

引用

2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Shi, Zhengang Wu, Chaofei Fu, Wenjie Tao, Peng Zhang, Linhao Gao, Bo State Grid Hebei Electric Power Co. Ltd. Marketing Service Center China

ISBN: (纸本)9781510672444

To enhance the performance of intelligent watt hour meters, a visual recognition and comparison system based on convolutional neural networks is proposed for intelligent watt hour meter chips. Firstly, the overall framework of the chip visual recognition comparison system is designed. Secondly, the hardware part of the system comprises the image acquisition module and image data transmission module of intelligent watt hour meter chips. In the software part, the classification function is selected based on the structural characteristics and operational principle of convolutional neural networks, and iterative training is used to complete the identification and comparison of smart meter chips. The experimental results demonstrate that this proposed system can significantly improve the accuracy of visual recognition and comparison, while also reducing the time consumption when compared to traditional recognition and comparison systems. © 2024 SPIE.

关键词： image acquisition

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Reversible Data Hiding in Encrypted image based on Adaptive Difference Prediction and Block Subdivision

Reversible Data Hiding in Encrypted Image based on Adaptive ...

引用

2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Zhang, Xuesheng Wang, Jing School of Computer and Information Security Guilin University of Electronic Science and Technology Guilin541004 China

ISBN: (纸本)9781510672444

Reversible Data Hiding in Encrypted images (RDHEI) embeds information while protecting the content of images from being leaked, allowing users to decrypt image content, extract embedded information, and losslessly recover the original content based on the key types they possess. It is a recent hot research area at the intersection of information hiding and encrypted computation, aiming to ensure both data security and the ability to hide information within images. However, inadequate utilization of image blocks in RDHEI results in a low embedding capacity of additional data. For this reason, this paper proposes a RDHEI based on adaptive difference prediction and block subdivision. At first, divide the image into equally sized blocks, and these blocks are encrypted to conceal the content of the image. For data hider, using adaptive the most significant bit(MSB) prediction to classify the available and unavailable blocks. Based on adaptive MSB prediction(AMP), adaptive difference prediction(ADP) is used to subdivide the unavailable blocks to vacate more room for data embedding. When receiver receives the embedded encrypted image, the embedded data or image can be decrypted according to its own key possession. Experimental results show that the proposed method has a significant effect on improving the embedding capacity. © 2024 SPIE.

关键词： Forecasting

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DHC-YOLO: An Improved YOLOv8 for Lesion Detection in Medical images* 2

DHC-YOLO: An Improved YOLOv8 for Lesion Detection in Medical...

引用

2nd International conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Ren, Shaojie Song, Jinmiao Yu, Long Tian, Shengwei College of Software Xinjiang University Urumqi China College of Network Center Xinjiang University Urumqi China

ISBN: (纸本)9798331543037

Detecting lesions in medical images is challenging due to variability in morphology, size, and boundaries across different diseases. We introduce DHC-YOLO, a novel approach that integrates multi-scale dilated attention, multi-head self-attention, and concatenation within the YOLOv8 framework. This method enhances semantic information aggregation across scales while minimizing redundancy in self-attention without adding computational costs. Our evaluation on brain tumor, colorectal polyp, and esophageal cancer datasets shows DHC-YOLO outperforms baseline YOLOv8 and other state-of-the-art models, achieving mAP50 and mAP50:95 scores of 88.3% and 73.5%, 88.8% and 67.2%, and 52.2% and 21.7%, respectively. These results highlight the effectiveness of DHC-YOLO for lesion detection. ©2024 IEEE.

关键词： image analysis

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Efficient vision transformer for dynamic embedding of multiscale features 3

Efficient vision transformer for dynamic embedding of multis...

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3rd International conference on machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Zhang, Mingrui Wang, Ronggui Yang, Juan Xue, Lixia School of Computer and Information Hefei University of Technology Anhui230601 China

ISBN: (纸本)9781510681880

vision Transformer (ViT) fully demonstrates the potential of the transformer architecture in the field of computer vision. However, the computational complexity is proportional to the length of the input sequence, thus limiting the application of transformers to high-resolution images. In order to improve the overall performance of vision Transformer, this paper proposes an efficient vision Transformer (MLVT) with dynamic embedding of multi-scale features, adopting the pyramid architecture, replacing the self-attention operation with linear self-attention, proposing a local attention enhancement module to address the problem of the dispersal of linear self-attention scores that ignores local correlation, and supplementing the local attention with the convolution of the self-attention-like computation. operation of self-attention-like computation is utilized to supplement the local attention. Aiming at the increase of feature dimension in pyramid architecture, the bottleneck of linear self-attention computation is changed from sequence length to feature dimension, and the linear self-attention with compressed feature dimension is proposed. In addition, since multi-scale inputs are crucial for processing image information, this paper proposes a flexible and learnable dynamic multi-scale feature embedding module, which dynamically adjusts the weights of different scale features according to the input image for fusion. A large number of experiments on image classification and target detection tasks show that competitive results are achieved while reducing the computational effort. © 2024 SPIE.

关键词： image fusion

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An Illumination Adaptive Underwater image Enhancement Method

An Illumination Adaptive Underwater Image Enhancement Method

引用

2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Zheng, Ruohan Miao, Jianming Zhang, Haosu Liu, Xinyu Tan, Dongxu School of Ocean Engineering and Technology Sun Yat-sen University Zhuhai519082 China Guangdong Institute of Intelligent Unmanned System Guangzhou511458 China

ISBN: (纸本)9781510672444

In underwater imagery, issues such as non-uniform illumination, blurriness, and low contrast are prevalent, significantly impacting the quality of captured images. In recent years, numerous researchers have delved into underwater image processing. Due to the intricacies of underwater environments, low-light images have different requirements compared to well-illuminated ones. However, existing algorithms often struggle to address the non-uniform illumination issues stemming from various lighting conditions in underwater settings. They also lack the capability to adaptively enhance underwater images with varying brightness. To tackle these challenges, we propose an adaptive illumination enhancement method for underwater images. This algorithm offers the capability to adaptively enhance underwater images suffering from detail blurriness based on their original brightness. Furthermore, it dynamically adjusts the parameters of the gamma function using the image's illumination component to augment color contrast. Experimental results demonstrate that our approach outperforms other algorithms, as evidenced by superior scores in UIQM metric. It effectively addresses edge blurriness and non-uniform illumination issues prevalent in underwater images captured under varying lighting conditions. © 2024 SPIE.

关键词： image enhancement

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Research on Surface Defect Classification Method of Hot Rolled Strip Steel Based on Comparative Learning

Research on Surface Defect Classification Method of Hot Roll...

引用

2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Zang, Xingshuai Zhang, Shengnan He, Yu School of Software Shenyang University of Technology 111 Shenliao West Road Shenyang110870 China

ISBN: (纸本)9781510672444

In response to the thin nature of hot rolled steel plates and strips, the vast majority of which are surface defects that can easily lead to production accidents, and limited by the challenges of insufficient datasets and a large amount of unlabeled data, this paper proposes a comparative learning method to solve the above problems. In terms of methods, a dual data augmentation strategy is adopted. Firstly, the original image is data enhanced through manual processing, and CycleGAN is introduced for style transfer to enrich the dataset. Then, ResNet152 network is used for feature extraction, and several comparative learning methods are applied to observe the accuracy of hot rolled strip defect detection. In the end, the improved comparative learning method in this article successfully improved the accuracy of surface defect classification for hot rolled strip steel. Through this research, we are committed to providing more reliable quality control methods for industrial production and reducing the risk of production accidents. © 2024 SPIE.

关键词： image classification

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Highresolution Remote Sensing image Classification With Limited Training Data

Highresolution Remote Sensing Image Classification With Limi...

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iranian machine vision and image processing (MVIP)

作者： Mehdi Ariaei Hassan Ghassemian Maryam Imani Image Processing and Information Analysis Lab Tarbiat Modares University Tehran Iran

Accurate classification of land cover from aerial images is one of the research topics in remote sensing and is also in high demand in industry. However, obtaining labeled data for training different classifiers that heavily depend on supervision is still a challenging and resource-intensive task. Unsupervised methods have emerged as a powerful alternative to overcome the limitations associated with labeled data. Such methods have a high ability to discover hidden patterns and structures in multi-spectral images and have the possibility of classifying various types of land cover without relying on labeled samples. Our research primarily involved the analysis of World-View3 satellite imagery. Our strategy involved creating an advanced pipeline that extracted features using autoencoders. Through this approach, the multispectral images' key characteristics are efficiently extracted. Subsequently, we implement transfer learning to re-train the model with a limited number of labeled data. By applying transfer learning, our pipeline significantly enhances the capability of multispectral image processing, enabling a more comprehensive and accurate interpretation of satellite imagery data. Finally, we evaluate our results not only by providing a confusion matrix but also through a visual comparison between the class map and the RGB composition of the MSI image.

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